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Call number: | 9783319509303:ONLINE Show nearby items on shelf |
Title: | Random Walks in the Quarter Plane Algebraic Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics |
Author(s): |
Guy Fayolle |
Date: | 2017 |
Edition: | 2nd ed. 2017 |
Size: | 1 online resource (XVII, 248 p. 17 illus p.) |
Contents: | Introduction and History -- I The General Theory. - Probabilistic Background. - Foundations of the Analytic Approach. - The Case of a Finite Group -- II Applications to Queueing Systems and Analytic Combinatorics -- A Two-Coupled |
Processor Model. - References | |
ISBN: | 9783319509303 |
Series: | eBooks |
Series: | Springer eBooks |
Series: | Springer 2017 package |
Keywords: | Mathematics , Mathematical statistics , Difference equations , Functional equations , Probabilities , Statistics , Mathematics , Probability Theory and Stochastic Processes , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien , Probability and Statistics in Computer Science , Difference and Functional Equations |
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Call number: | 9783319458090:ONLINE Show nearby items on shelf |
Title: | Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry |
Author(s): | |
Date: | 2017 |
Size: | 1 online resource (VIII, 295 p. 106 illus., 83 illus. in color p.) |
Contents: | Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry -- The analysis of peptide-centric mass spectrometry |
data utilizing information about the expected isotope distribution -- Probabilistic and likelihood-based methods for protein identification from MS/MS data -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data | |
Processing -- Mass Spectrometry Analysis Using MALDIquant -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry -- The analysis of human serum albumin proteoforms using | |
compositional framework -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection -- Statistical approach for biomarker discovery using label-free LC-MS data - an overview -- Bayesian posterior integration for | |
classification of mass spectrometry data -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies -- Robust and confident predictor selection in metabolomics -- On the combination of | |
omics data for prediction of binary Outcomes -- Statistical analysis of lipidomics data in a case-control study | |
ISBN: | 9783319458090 |
Series: | eBooks |
Series: | Springer eBooks |
Series: | Springer 2017 package |
Keywords: | Statistics , Analytical chemistry , Mathematical statistics , Bioinformatics , Biostatistics , Metabolism , Statistics , Statistics for Life Sciences, Medicine, Health Sciences , Biostatistics , Metabolomics , Computational Biology/Bioinformatics , Analytical Chemistry , Probability and Statistics in Computer Science |
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Call number: | 9783319434766:ONLINE Show nearby items on shelf |
Title: | Discrete Probability Models and Methods Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding |
Author(s): |
Pierre Brémaud |
Date: | 2017 |
Size: | 1 online resource (XIV, 559 p. 92 illus p.) |
Contents: | Introduction -- 1.Events and probability -- 2.Random variables -- 3.Bounds and inequalities -- 4.Almost-sure convergence -- 5.Coupling and the variation distance -- 6.The probabilistic method -- 7.Codes and trees -- 8.Markov chains -- |
9.Branching trees -- 10.Markov fields on graphs -- 11.Random graphs -- 12.Recurrence of Markov chains -- 13.Random walks on graphs -- 14.Asymptotic behaviour of Markov chains -- 15.Monte Carlo sampling -- 16. Convergence rates -- | |
Appendix -- Bibliography | |
ISBN: | 9783319434766 |
Series: | eBooks |
Series: | Springer eBooks |
Series: | Springer 2017 package |
Keywords: | Mathematics , Computer communication systems , Coding theory , Mathematical statistics , Probabilities , Graph theory , Mathematics , Probability Theory and Stochastic Processes , Probability and Statistics in Computer Science , Graph Theory , Coding and Information Theory , Computer Communication Networks |
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Call number: | 9781493965724:ONLINE Show nearby items on shelf |
Title: | Statistics and Analysis of Scientific Data |
Author(s): |
Massimiliano Bonamente |
Date: | 2017 |
Edition: | 2nd ed. 2017 |
Size: | 1 online resource (XVII, 318 p. 40 illus., 4 illus. in color p.) |
Contents: | Theory of Probability -- Random Variables and Their Distribution -- Sum and Functions of Random Variables -- Estimate of Mean and Variance and Confidence Intervals -- Median, Weighted Mean and Linear Average (NEW) -- Distribution |
Function of Statistics and Hypothesis Testing -- Maximum Likelihood Fit to a Two-Variable Dataset -- Goodness of Fit and Parameter Uncertainty -- Systematic Errors and Intrinsic Scatter (NEW) -- Fitting Data with Bivariate Errors (NEW) | |
-- Comparison Between Models -- Monte Carlo Methods -- Markov Chains and Monte Carlo Markov Chains -- Statistics for Business Sciences and Addition of Multi–Variate Analysis (NEW) | |
ISBN: | 9781493965724 |
Series: | eBooks |
Series: | Springer eBooks |
Series: | Springer 2017 package |
Keywords: | Physics , System theory , Statistics , Applied mathematics , Engineering mathematics , Physics , Mathematical Methods in Physics , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien , Statistics for Business/Economics/Mathematical Finance/Insurance , Appl.Mathematics/Computational Methods of Engineering , Complex Systems , Statistical Physics and Dynamical Systems |
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Call number: | SPRINGER-2016-9789811008894:ONLINE Show nearby items on shelf |
Title: | Examples in Parametric Inference with R |
Author(s): |
Ulhas Jayram Dixit |
Date: | 2016 |
Size: | 1 online resource (423 p.) |
Note: | 10.1007/978-981-10-0889-4 |
Contents: | Prerequisite -- Chapter 1. Sufficiency and Completeness -- Chapter 2. Unbiased Estimation -- Chapter 3. Moment and Maximum Likelihood Estimators -- Chapter 4. Bound for the Variance -- Chapter 5. Consistent Estimator -- Chapter 6. Bayes Estimator - - Chapter 7. Most Powerful Test -- Chapter 8. Unbiased and Other Tests -- Bibliography |
ISBN: | 9789811008894 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Statistics , Mathematical statistics , Statistics , Statistical Theory and Methods , Statistics and Computing/Statistics Programs , Probability and Statistics in Computer Science |
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Call number: | SPRINGER-2016-9784431559788:ONLINE Show nearby items on shelf |
Title: | Information Geometry and Its Applications |
Author(s): |
Shun-ichi Amari |
Date: | 2016 |
Edition: | 1st ed. 2016 |
Size: | 1 online resource (373 p.) |
Note: | 10.1007/978-4-431-55978-8 |
Contents: | 1 Manifold, Divergence and Dually Flat Structure -- 2 Exponential Families and Mixture Families of Probability -- 3 Invariant Geometry of Manifold of Probability -- 4 α-Geometry, Tsallis q-Entropy and Positive-Definite -- 5 Elements of Differentia l Geometry -- 6 Dual Affine Connections and Dually Flat Manifold -- 7 Asymptotic Theory of Statistical Inference -- 8 Estimation in the Presence of Hidden Variables -- 9 Neyman–Scott Problem -- 10 Linear Systems and Time Series -- 11 Machine Learning -- 12 Natural Gradient Learning and its Dynamics in Singular -- 13 Signal Processing and Optimization -- Index |
ISBN: | 9784431559788 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | Applied Mathematical Sciences: 194 |
Keywords: | Mathematics , Computer science , Computer mathematics , Differential geometry , Statistics , Mathematics , Differential Geometry , Mathematical Applications in Computer Science , Statistical Theory and Methods |
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Call number: | SPRINGER-2016-9783319327747:ONLINE Show nearby items on shelf |
Title: | Estimation and Testing Under Sparsity École d'Été de Probabilités de Saint-Flour XLV – 2015 |
Author(s): |
Sara van de Geer |
Date: | 2016 |
Size: | 1 online resource (274 p.) |
Note: | 10.1007/978-3-319-32774-7 |
Contents: | 1 Introduction -- The Lasso -- 3 The square-root Lasso -- 4 The bias of the Lasso and worst possible sub-directions -- 5 Confidence intervals using the Lasso -- 6 Structured sparsity -- 7 General loss with norm-penalty -- 8 Empirical process theory for dual norms -- 9 Probability inequalities for matrices -- 10 Inequalities for the centred empirical risk and its derivative -- 11 The margin condition -- 12 Some worked-out examples -- 13 Brouwer’s fixed point theorem and sparsity -- 14 Asymptotical ly linear estimators of the precision matrix -- 15 Lower bounds for sparse quadratic forms -- 16 Symmetrization, contraction and concentration -- 17 Chaining including concentration -- 18 Metric structure of convex hulls |
ISBN: | 9783319327747 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | Lecture Notes in Mathematics: 2159 |
Keywords: | Mathematics , Mathematical statistics , Probabilities , Statistics , Mathematics , Probability Theory and Stochastic Processes , Statistical Theory and Methods , Probability and Statistics in Computer Science |
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Call number: | SPRINGER-2016-9783319306209:ONLINE Show nearby items on shelf |
Title: | Introduction to Probability with Statistical Applications |
Author(s): |
Géza Schay |
Date: | 2016 |
Edition: | 2nd ed. 2016 |
Size: | 1 online resource (385 p.) |
Note: | 10.1007/978-3-319-30620-9 |
Contents: | Introduction -- The Algebra of Events -- Combinatorial Problems -- Probabilities -- Random Variables -- Expectation, Variance, Moments -- Some Special Distributions -- The Elements of Mathematical Statistics |
ISBN: | 9783319306209 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Mathematics , Mathematical statistics , Measure theory , Applied mathematics , Engineering mathematics , Probabilities , Statistics , Mathematics , Probability Theory and Stochastic Processes , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien , Probability and Statistics in Computer Science , Measure and Integration , Applications of Mathematics |
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Call number: | SPRINGER-2016-9783319301907:ONLINE Show nearby items on shelf |
Title: | Rabi N. Bhattacharya Selected Papers |
Author(s): | |
Date: | 2016 |
Size: | 1 online resource (711 p.) |
Note: | 10.1007/978-3-319-30190-7 |
Contents: | Part I: Modes of Approximation -- Hall, Contributions of Rabi Bhattacharya to the Central Limit Theory and Normal Approximation -- Yoshida, Asymptotic Expansions for Stochastic Processes -- Shao, An Introduction to Normal Approximation -- Reprints - - Part II: Large Time Asymptotics for Markov Processes I: Diffusion -- Varadhan, Martingale Methods for the Central Limit Theorem -- Kurtz, Ergodicity and Central Limit Theorems for Markov Processes -- Reprints -- Part III: Large Time Asymptotics for Mark ov Processes II: Dynamical Systems and Iterated Maps -- Athreya, Dynamical Systems, IID Random Iterations, and Markov Chains -- Waymire, Random Dynamical Systems and Selected Works of Rabi Bhattacharya -- Reprints -- Part IV: Stochastic Foundations in App lied Sciences I: Economics -- Kamihigashi, Stachurski, Stability Analysis for Random Dynamical Systems in Economics -- Roy, Some Economic Applications of Recent Advances in Random Dynamical Systems -- Reprints -- Part V: Stochastic |
Foundations in Applied Sciences II: Geophysics -- Thomann, Waymire, Advection-Dispersion in Fluid Media and Selected Works of Rabi Bhattacharya -- Flandoli, Romito, Cascade Representations for the Navier-Stokes Equations -- Reprints -- Part VI: Stoc hastic Foundations in Applied Sciences III: Statistics -- Dryden, Le, Preston, Wood, Nonparametric Statistical Methods on Manifolds -- Huckemann, Hotz, Nonparametric Statistics on Manifolds and Beyond -- Reprints | |
ISBN: | 9783319301907 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Mathematics , Probabilities , Statistics , Mathematics , Probability Theory and Stochastic Processes , Statistics for Business/Economics/Mathematical Finance/Insurance , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien |
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Call number: | SPRINGER-2016-9783319287256:ONLINE Show nearby items on shelf |
Title: | Time Series Analysis and Forecasting Selected Contributions from the ITISE Conference |
Author(s): | |
Date: | 2016 |
Size: | 1 online resource (49 p.) |
Note: | 10.1007/978-3-319-28725-6 |
Contents: | Main Topics: Time Series Analysis and Forecasting -- Advanced method and on-Line Learning in time series -- High Dimension and Complex/Big Data -- Forecasting in real problem |
ISBN: | 9783319287256 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Statistics , Mathematical statistics , Econometrics , Statistics , Statistics for Business/Economics/Mathematical Finance/Insurance , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien , Econometrics , Probability and Statistics in Computer Science |
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Call number: | SPRINGER-2016-9783319263113:ONLINE Show nearby items on shelf |
Title: | Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis with Applications |
Author(s): |
Chiara Brombin |
Date: | 2016 |
Edition: | 1st ed. 2016 |
Size: | 1 online resource (27 p.) |
Note: | 10.1007/978-3-319-26311-3 |
Contents: | Part I Offset Normal Distribution for Dynamic Shapes -- Basic Concepts and Definitions -- Shape Inference and the Offset-Normal Distribution -- Dynamic Shape Analysis Through the Offset-Normal Distribution -- Part II Combination-Based Permutation T ests for Shape Analysis -- Parametric and Non-Parametric Testing of Mean Shapes -- Applications of NPC Methodology -- Shape Inference and the Offset-Normal Distribution. |
ISBN: | 9783319263113 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Keywords: | Statistics , Mathematical statistics , Computer mathematics , Statistics , Statistical Theory and Methods , Probability and Statistics in Computer Science , Computational Mathematics and Numerical Analysis |
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Call number: | SPRINGER-2016-9783319201764:ONLINE Show nearby items on shelf |
Title: | Statistical Methods for Data Analysis in Particle Physics |
Author(s): |
Luca Lista |
Date: | 2016 |
Edition: | 1st ed. 2016 |
Size: | 1 online resource (59 p.) |
Note: | 10.1007/978-3-319-20176-4 |
Contents: | Preface -- Probability theory -- Probability Distribution Functions -- Bayesian approach to probability -- Random numbers and Monte Carlo Methods -- Parameter estimate -- Confidence intervals -- Hypothesis tests -- Upper Limits -- |
Bibliography | |
ISBN: | 9783319201764 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | Lecture Notes in Physics: 909 |
Keywords: | Physics , Elementary particles (Physics) , Quantum field theory , Physical measurements , Measurement , Statistics , Physics , Elementary Particles, Quantum Field Theory , Measurement Science and Instrumentation , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Scien |
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Call number: | SPRINGER-2016-9783319072548:ONLINE Show nearby items on shelf |
Title: | Elements of Probability and Statistics An Introduction to Probability with de Finetti’s Approach and to Bayesian Statistics |
Author(s): |
Francesca Biagini |
Date: | 2016 |
Edition: | 1st ed. 2016 |
Size: | 1 online resource (27 p.) |
Note: | 10.1007/978-3-319-07254-8 |
Contents: | 1 Random numbers -- 2 Discrete distributions -- 3 One-dimensional absolutely continuous distributions -- 4 Multi-dimensional absolutely continuous distributions -- 5 Convergence of distributions -- 6 Discrete time Markov chains -- 7 Continuous time Markov chains -- 8 Statistics -- 9 Combinatorics -- 10 Discrete distributions -- 11 One-dimensional absolutely continuous distributions -- 12 Absolutely continuous and multivariate distributions -- 13 Markov chains -- 14 Statistics -- 15 Elements of comb inatorics -- 16 Relations between discrete and absolutely continuous distributions -- 17 Some discrete distributions -- 18 Some one-dimensional absolutely continuous distributions -- 19 The normal distribution -- 20 Stirling's formula -- 21 Elements of an alysis -- 22 Bidimensional integrals |
ISBN: | 9783319072548 |
Series: | eBooks |
Series: | SpringerLink (Online service) |
Series: | Springer eBooks |
Series: | UNITEXT: 98 |
Keywords: | Mathematics , Business mathematics , Mathematical statistics , Probabilities , Physics , Statistics , Applied mathematics , Engineering mathematics , Mathematics , Probability Theory and Stochastic Processes , Statistical Theory and Methods , Probability and Statistics in Computer Science , Business Mathematics , Mathematical Methods in Physics , Appl.Mathematics/Computational Methods of Engineering |
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Call number: | SPRINGER-2014-9783319077796:ONLINE Show nearby items on shelf |
Title: | Geometric Modeling in Probability and Statistics [electronic resource] |
Author(s): |
Ovidiu Calin Constantin Udrite |
Date: | 2014 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | This book covers topics of Informational Geometry, a field which deals with the differential geometric study of the manifold probability density functions. This is a field that is increasingly attracting the interest ofresearchers from many different areas of science, including mathematics, statistics, geometry, computer science, signal processing, physics and neuroscience. It is the authors hope that the present book will be a valuable referencefor researchers and graduate students in one of the afo rementioned fields. This textbook is a unified presentation of differential geometry and probability theory, and constitutes a text for a course directed at graduate or advancedundergraduate students interested in applications of differential geometry in probability and statistics. The book contains over 100 proposed exercises meant to help students deepen their understanding, and it is accompanied bysoftware that is able to provide numerical computations of several information geometric objects. The read er will understand a flourishing field of mathematics in which very few books have been written so far |
Contents: | Part I: The Geometry of Statistical Models |
Statistical Models | |
Explicit Examples | |
Entropy on Statistical Models | |
KullbackLeibler Relative Entropy | |
Informational Energy | |
Maximum Entropy Distributions | |
Part II: Statistical Manifolds | |
An Introduction to Manifolds | |
Dualistic Structure | |
Dual Volume Elements | |
Dual Laplacians | |
Contrast Functions Geometry | |
Contrast Functions on Statistical Models | |
Statistical Submanifolds | |
Appendix A: Information Geometry Calculator | |
ISBN: | 9783319077796 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Geometry , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2014-9783319044866:ONLINE Show nearby items on shelf |
Title: | Risk - A Multidisciplinary Introduction [electronic resource] |
Author(s): |
Claudia Klppelberg Daniel Straub Isabell M Welpe |
Date: | 2014 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | This is a unique book addressing the integration of risk methodology from various fields. It stimulates intellectual debate and communication across disciplines, promotes better risk management practices and contributes to thedevelopment of risk mana gement methodologies. Book chapters explain fundamental risk models and measurement, and address risk and security issues from diverse areas such as finance and insurance, health sciences, life sciences,engineering and information science. Integrated Risk Sciences is an emerging field, that considers risks in different fields aiming at a common language, and at sharing and improving methods developed in different fields. Readersshould have a Bachelor degree and at least one basic university course in stat istics and probability. The main goal of the book is to provide basic knowledge on risk and security in a common language the authors have taken particularcare to ensure that each chapter can be understood by doctoral students and researchers across disci plines. Each chapter provides simple case studies and examples, open research questions and discussion points, and a selectedbibliography inviting the reader to further studies |
Contents: | Introduction |
Part One. Risk in History and Science: Zachmann, K.: Risk in historical perspective: concepts, contexts, and conjunctions | |
Ltge, C., Schnebel, E., Westphal, N.: Risk management and business ethics: integrating the human factor | |
Straub, D., Welpe, I.: Decision | |
making under risk: a normative and behavioral perspective | |
Mainzer, K.: The new role of mathematical modelling and its importance for society | |
Part Two. Quantitative Risk Methodology: Biagini, F. , Meyer | |
Brandis, T. and Svindland, G. :The mathematical concept of risk | |
Fasen, V., Klppelberg, C., Menzel, A.: Qu | |
ISBN: | 9783319044866 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Geology , Computer science , Distribution (Probability theory) , Statistics , System safety , Climatic changes |
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Call number: | SPRINGER-2014-9783319024356:ONLINE Show nearby items on shelf |
Title: | Basic Concepts in Computational Physics [electronic resource] |
Author(s): |
Benjamin A. Stickler Ewald Schachinger |
Date: | 2014 |
Publisher: | Cham : Springer International Publishing : Imprint: Springer |
Size: | 1 online resource |
Note: | With the development of ever more powerful computers a new branch of physics and engineering evolved over the last few decades: Computer Simulation or Computational Physics. It serves two main purposes: - Solution of complexmathematical problems such as, differential equations, minimization/optimization, or high-dimensional sums/integrals. - Direct simulation of physical processes, as for instance, molecular dynamics or Monte-Carlo simulation ofphysical/chemical/technical processes. Consequently, the book is divided into two main parts: Deterministic methods and stochastic methods. Based on concrete problems, the first part discusses numerical differentiation and integration,and the treatment of ordinary differential equations. This is augmented by n otes on the numerics of partial differential equations. The second part discusses the generation of random numbers, summarizes the basics of stochastics whichis then followed by the introduction of various Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. All this is again augmented by numerous applications from physics. The final two chapters on Data Analysisand Stochastic Optimization share the two main topics as a common denominator. The book offers a number of appendices to provide the reader with more detailed information on various topics discussed in the main part. Nevertheless, thereader should be familiar with the most important concepts of statistics and probability theory albeit two appendices have been dedicated to provide a rudimentary discussion |
Contents: | Some Basic Remarks |
Part I Deterministic Methods: Numerical Differentiation | |
Numerical Integration | |
The KEPLER Problem | |
Ordinary Differential Equations Initial Value Problems | |
The Double Pendulum | |
Molecular Dynamics | |
Numerics of Ordinary Differential Equations | |
Boundary Value Problems | |
The One | |
Dimensional Stationary Heat Equation | |
The One | |
Dimensional Stationary SCHRDINGER Equation | |
Numerics of Partial Differential Equations | |
Part II Stochastic Methods | |
Pseudo Random Number Generators | |
Random Sampling Methods | |
A Brief Introduction to Monte | |
Carlo Methods | |
The | |
ISBN: | 9783319024356 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Physics and Astronomy (Springer-11651) |
Keywords: | Chemistry , Computer science Mathematics , Engineering mathematics |
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Call number: | SPRINGER-2014-9781447153610:ONLINE Show nearby items on shelf |
Title: | Probability Theory [electronic resource] : A Comprehensive Course |
Author(s): |
Achim Klenke |
Date: | 2014 |
Edition: | 2nd ed. 2014 |
Publisher: | London : Springer London : Imprint: Springer |
Size: | 1 online resource |
Note: | This second edition of the popular textbook contains a comprehensive course in modern probability theory. Overall, probabilistic concepts play an increasingly important role in mathematics, physics, biology, financial engineeringand computer science. They help us in understanding magnetism, amorphous media, genetic diversity and the perils of random developments at financial markets, and they guide us in constructing more efficient algorithms. To addressthese concepts, the title covers a wide variet y of topics, many of which are not usually found in introductory textbooks, such as: limit theorems for sums of random variables martingales percolation Markov chains andelectrical networks construction of stochastic processes Poisson point process and infinite divisibility large deviation principles and statistical physics Brownian motion stochastic integral and stochasticdifferential equations. The theory is developed rigorously and in a self-contained way, with the chapters on measure theory interlaced with the probabilistic chapters in order to display the power of the abstract concepts inprobability theory. This second edition has been carefully extended and includes many new features. It contains updated figures (over 50), computer simulat ions and some difficult proofs have been made more accessible. A wealth ofexamples and more than 270 exercises as well as biographic details of key mathematicians support and enliven the presentation. It will be of use to students and researchers in mathe matics and statistics in physics, computer science,economics and biology |
Contents: | Basic Measure Theory |
Independence | |
Generating Functions | |
The Integral | |
Moments and Laws of Large Numbers | |
Convergence Theorems | |
Lp | |
Spaces and the RadonNikodym Theorem | |
Conditional Expectations | |
Martingales | |
Optional Sampling Theorems | |
Martingale Convergence Theorems and Their Applications | |
Backwards Martingales and Exchangeability | |
Convergence of Measures | |
Probability Measures on Product Spaces | |
Characteristic Functions and the Central Limit Theorem | |
Infinitely Divisible Distributions | |
Markov Chains | |
Convergence of Markov Chains | |
Markov Chains and Electr | |
ISBN: | 9781447153610 |
Series: | eBooks |
Series: | SpringerLink |
Series: | Universitext, 0172-5939 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differentiable dynamical systems , Functional analysis , Distribution (Probability theory) |
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Call number: | SPRINGER-2013-9783642339110:ONLINE Show nearby items on shelf |
Title: | Fractional Derivatives for Physicists and Engineers [electronic resource] : Background and Theory |
Author(s): |
Vladimir V Uchaikin |
Date: | 2013 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The first derivative of a particle coordinate means its velocity, the second means its acceleration, but what does a fractional order derivative mean? Where does it come from, how does it work, where does it lead to? Thetwo-volume book written on hig h didactic level answers these questions. Fractional Derivatives for Physicists and Engineers The first volume contains a clear introduction into such a modern branch of analysis as the fractionalcalculus. The second develops a wide panorama of applicatio ns of the fractional calculus to various physical problems. This book recovers new perspectives in front of the reader dealing with turbulence and semiconductors, plasma andthermodynamics, mechanics and quantum optics, nanophysics and astrophysics. The bo ok is addressed to students, engineers and physicists, specialists in theory of probability and statistics, in mathematical modeling and numericalsimulations, to everybody who doesn't wish to stay apart from the new mathematical methods becoming more and more popular. Prof. Vladimir V. UCHAIKIN is a known Russian scientist and pedagogue, a Honored Worker of Russian High School,a member of the Russian Academy of Natural Sciences. He is the author of about three hundreds articles and more than a dozen books (mostly in Russian) in Cosmic ray physics, Mathematical physics, Levy stable statistics, Monte Carlomethods with applications to anomalous processes in complex systems of various levels: from quantum dots to the Milky Way galaxy |
Note: | Springer eBooks |
Contents: | Physical Basics |
Fractional Derivatives | |
Fractional Equations | |
Applications | |
Mechanics | |
Kinetics | |
Electrodynamics | |
Atomic Physics | |
Space Physics | |
ISBN: | 9783642339110 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Nonlinear Physical Science, 1867-8440 |
Series: | Physics and Astronomy (Springer-11651) |
Keywords: | Computer science Mathematics |
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Call number: | SPRINGER-2013-9783034804905:ONLINE Show nearby items on shelf |
Title: | High Dimensional Probability VI [electronic resource] : The Banff Volume |
Author(s): |
Christian Houdr David M Mason Jan Rosiski Jon A Wellner |
Date: | 2013 |
Publisher: | Basel : Springer Basel : Imprint: Birkhuser |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This is a collection of papers by participants at theHigh Dimensional Probability VI Meeting held from October 9-14, 2011 at the Banff International Research Station in Banff, Alberta, Canada. High Dimensional Probability(HDP) is an area of mathemati cs that includes the study of probability distributions and limit theorems in infinite dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it hasresulted in the creation of powerful new tools and p erspectives, whose range of application has led to interactions with other areas of mathematics, statistics, and computer science. These include random matrix theory, nonparametricstatistics, empirical process theory, statistical learning theory, concentr ation of measure phenomena, strong and weak approximations, distribution function estimation in high dimensions, combinatorial optimization, and random graphtheory. The papers in this volumeshow that HDP theory continues to develop new tools, methods, tec hniques and perspectives to analyze the random phenomena. Both researchers and advanced students will find this book of great use forlearning about new avenues of research |
Note: | Springer eBooks |
ISBN: | 9783034804905 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Progress in Probability : v66 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Mathematical optimization , Distribution (Probability theory) |
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Call number: | SPRINGER-2013-9781461462279:ONLINE Show nearby items on shelf |
Title: | Understanding Statistics Using R [electronic resource] |
Author(s): |
Randall Schumacker Sara Tomek |
Date: | 2013 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class. The chapter content is ordered along the lines of many popular statistics books so it should beeasy to supplement the cont ent and exercises with class lecture materials. The book contains R script programs to demonstrate important topics and concepts covered in a statistics course, including probability, random sampling,population distribution types, role of the Central Limi t Theorem, creation of sampling distributions for statistics, and more. The chapters contain T/F quizzes to test basic knowledge of the topics covered. In addition, the bookchapters contain numerous exercises with answers or solutions to the exercises pro vided. The chapter exercises reinforce an understanding of the statistical concepts presented in the chapters. An instructor can select any of thesupplemental materials to enhance lectures and/or provide additional coverage of concepts and topics in their statistics book. This book uses the R statistical package which contains an extensive library of functions. The R softwareis free and easily downloaded and installed. The R programs are run in the R Studio software which is a graphical user interface for Windows. The R Studio software makes accessing R programs, viewing output from the exercises, andgraphical displays easier to manage. The first chapter of the book covers the fundamentals of the R statistical package. This includes installation of R and R Studio, accessing R packages and libraries of functions. The chapteralso covers how to access manuals and technical documentation, as well as, basic R commands used in the R script programs in the chapters. This chapter is important for the instructor t o master so that the software can be installedand the R script programs run. The R software is free so students can also install the software and run the R script programs in |
Note: | Springer eBooks |
Contents: | R Fundamentals |
Probability | |
Statistical Theory | |
Frequency Distributions | |
Central Tendency and Dispersion | |
Statistical Distributions | |
Hypothesis Testing | |
Chi | |
Square Test | |
z | |
test | |
t | |
test | |
F | |
test | |
Correlation | |
Linear Regression | |
Replication of Results | |
Synthesis of Findings | |
Glossary | |
Appendix | |
Author Index | |
Subject Index | |
ISBN: | 9781461462279 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Computer science , Computer simulation |
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Call number: | SPRINGER-2013-9781461440574:ONLINE Show nearby items on shelf |
Title: | Informal Introduction to Stochastic Processes with Maple [electronic resource] |
Author(s): |
Jan Vrbik Paul Vrbik |
Date: | 2013 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The book presents an introduction to Stochastic Processes including Markov Chains, Birth and Death processes, Brownian motion andAutoregressive models. The emphasis is on simplifying both the underlying mathematics and theconceptual understanding of random processes. In particular, non-trivial computations are delegated to a computer-algebra system, specifically Maple (although other systems can be easily substituted). Moreover, great care istaken to properly introduce the required mathematical tools (such as difference equations and generating functions) so that even students with only a basic mathematical background will find the book self-contained. Manydetailed examples are given throughout the text to facilitate and reinforce learning. Jan Vrbik has been a Professor of Mathematics and Statistics at Brock University in St Catharines, Ontario, Canada, since 1982. Paul Vrbik iscurrently a PhD candidate in Computer Science at the University of Western Ontario in London, Ontario, Canada |
Note: | Springer eBooks |
Contents: | Contents |
Preface | |
Finite Markov Chains | |
Finite Markov Chains II | |
Branching Processes | |
Renewal Theory | |
Poisson Process | |
Birth and Death Processes I | |
Birth and Death Processes II | |
Continuous | |
Time Markov Chains | |
Brownian Motion | |
Autoregressive Models | |
Basic Probability Review | |
Maple Programming | |
References | |
ISBN: | 9781461440574 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Universitext, 0172-5939 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2013-9781447153436:ONLINE Show nearby items on shelf |
Title: | Probability Models [electronic resource] |
Author(s): |
John Haigh |
Date: | 2013 |
Edition: | 2nd ed. 2013 |
Publisher: | London : Springer London : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation. It describes how to set up and analyse models of real-life phenomena that involve elements of chance.Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise. Applicationsinclude branching processes, random walks, Mark ov chains, queues, renewal theory, and Brownian motion. This popular second edition textbook contains many worked examples and several chapters have been updated and expanded. Somemathematical knowledge is assumed. The reader should have the ability to wo rk with unions, intersections and complements of sets a good facility with calculus, including integration, sequences and series and appreciation of thelogical development of an argument. Probability Models is designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics |
Note: | Springer eBooks |
Contents: | Probability Spaces |
Conditional Probability and Independence | |
Common Probability Distributions | |
Random Variables | |
Sums of Random Variables | |
Convergence and Limit Theorems | |
Stochastic Processes in Discrete Time | |
Stochastic Processes in Continuous Time | |
Appendix: Common Distributions and Mathematical Facts | |
ISBN: | 9781447153436 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Undergraduate Mathematics Series, 1615-2085 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Computer simulation , Distribution (Probability theory) , Operations research |
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Call number: | SPRINGER-2013-9781447150794:ONLINE Show nearby items on shelf |
Title: | Stream Ciphers [electronic resource] |
Author(s): |
Andreas Klein |
Date: | 2013 |
Publisher: | London : Springer London : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | In cryptography, ciphers is the technical term for encryption and decryption algorithms. They are an important sub-family that features high speed and easy implementation and are an essential part of wireless internet and mobilephones. Unlike block ciphers, stream ciphers work on single bits or single words and need to maintain an internal state to change the cipher at each step. Typically stream ciphers can reach higher speeds than block ciphers but theycan be more vulnerable to attack. Here, mathe matics comes into play. Number theory, algebra and statistics are the key to a better understanding of stream ciphers and essential for an informed decision on their safety. Since thetheory is less developed, stream ciphers are often skipped in books on cryptography. This book fills this gap. It covers the mathematics of stream ciphers and its history, and also discusses many modern examples and their robustnessagainst attacks. Part I covers linear feedback shift registers, non-linear combinations of LF SRs, algebraic attacks and irregular clocked shift registers. Part II studies some special ciphers including the security of mobile phones,RC4 and related ciphers, the eStream project and the blum-blum-shub generator and related ciphers. Stream Ciphers r equires basic knowledge of algebra and linear algebra, combinatorics and probability theory and programming.Appendices in Part III help the reader with the more complicated subjects and provides the mathematical background needed. It covers, for example, complexity, number theory, finite fields, statistics, combinatorics. Stream Ciphersconcludes with exercises and solutions and is directed towards advanced undergraduate and graduate students in mathematics and computer science |
Note: | Springer eBooks |
Contents: | Introduction to Stream Ciphers |
Linear Feedback Shift Registers | |
Non | |
linear Combinations of LFSRs | |
Correlation Attacks | |
BDD | |
Based Attacks | |
Algebraic Attacks | |
Irregular Clocked Shift Registers | |
The Security of Mobile Phones (GSM) | |
RC4 and Related Ciphers | |
The eStream Project | |
The Blum | |
Blum | |
Shub Generator and Related Ciphers | |
Mathematical Background | |
Part IV Exercises with Solutions | |
ISBN: | 9781447150794 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computational complexity , Algorithms |
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Call number: | SPRINGER-2012-9781461437130:ONLINE Show nearby items on shelf |
Title: | Fundamentals of Queuing Systems [electronic resource] : Statistical Methods for Analyzing Queuing Models |
Author(s): |
Nick T Thomopoulos |
Date: | 2012 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Waiting in lines is a staple of everyday human life.Without really noticing, we are doing it when we go to buy a ticket at a movie theater, stop at a bank to make an account withdrawal, or proceed to checkout a purchase fromone of our favorite depart ment stores.Oftentimes, waiting lines are due to overcrowded, overfilling, or congestionany time there is more customer demand for a service than can be provided, a waiting line forms.Queuingsystems is a term used to describe the methods and techniques mo st ideal for measuring the probability and statistics of a wide variety of waiting line models.This book provides an introduction to basic queuing systems, such asM/M/1 and its variants, as well as newer concepts like systems with priorities, networks of queues, and general service policies.Numerical examples are presented to guide readers into thinking about practical real-worldapplications, and students and researcherswill be able to apply the methods learned to designing queuing systems that extend bey ond the classroom.Very little has been published in the area of queuing systems, and this volume willappeal to graduate-level students, researchers, and practitioners in the areas of management science, applied mathematics, engineering, computer science, and statistics. |
Note: | Springer eBooks |
Contents: | Introduction |
Preliminary Concepts | |
One Server, Infinite Queue (M/M/1) | |
One Server, Finite Queue (M/M/1/N) | |
One Server, No Queue (M/M/1/1) | |
Multi Servers, Infinite Queue (M/M/k) | |
Multi Servers, Finite Queue (M/M/k/N) | |
Multi Servers, No Queue (M/M/k/k) | |
One Server, Arbitrary Service (M/G/1) | |
2 Populations, One Server, Arbitrary Service (M/G/1/2) | |
M Machines, One Repairman (M/M/1/M) | |
M Machines, R Repairmen (M/M/R/M) | |
One Server, Repeat Service (M/M/1/q) | |
Multi Servers, Repeat Service (M/M/k/) | |
Tandem Queues (M/M/1 : M/M/1) | |
Priority System, One Server, Infinite Q | |
ISBN: | 9781461437130 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Economics Statistics |
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Call number: | SPRINGER-2012-9781461419662:ONLINE Show nearby items on shelf |
Title: | Probability Approximations and Beyond [electronic resource] |
Author(s): |
Andrew Barbour Hock Peng Chan David Siegmund |
Date: | 2012 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | In June 2010, a conference, Probability Approximations and Beyond, was held at the National University of Singapore (NUS), in honor of pioneering mathematician Louis Chen. Louis is perhaps best known for his elegant Poissonapproximation method, devel oped from Steins original approach to normal approximation. Another of his important contributions has been to turn Steins concentration inequality idea into an effective tool for providing error boundsfor the normal approximation in many settings, and in particular for sums of random variables exhibiting only local dependence. The conference attracted a large audience that came to pay homage to Louis, and to hear presentations bycolleagues who have worked with him in special ways over the past 40 years. The papers in this volume attest to how Louis Chens ideas have influenced and continue to influence such diverse areas as molecular biology and computerscience. He himself has developed applications of his work on Poisson approximation to problems of sign al detection in computational biology. The original papers contained in this book provide historical context for Louiss work,alongside commentary on some of his major contributions by noteworthy statisticians and mathematicians working today. The papers i n this volume attest to how Louis Chens ideas have influenced and continue to influence such diverseareas as molecular biology and computer science. He himself has developed applications of his work on Poisson approximation to problems of signal detection in computational biology. The original papers contained in this book providehistorical context for Louiss work, alongside commentary on some of his major contributions by noteworthy statisticians and mathematicians working today |
Note: | Springer eBooks |
Contents: | Couplings for Irregular Combinatorial Assemblies |
Berry | |
Esseen Inequality for Unbounded Exchangeable Pairs | |
Clubbed Binomial Approximation for the Lightbulb Process | |
Coverage of Random Discs Driven by a Posson Point Process | |
On the Optimality of Stein Factors | |
Basic Estimates of Stability Rate for One | |
dimensional Diffusions | |
Trend Analysis of Extreme Values | |
Renormalizations in White Noise Analysis | |
M | |
dependence Approximation for Dependent Random Variables | |
Variable Selection for Classification and Regression in Large p, Small n Problems | |
ISBN: | 9781461419662 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Lecture Notes in Statistics, 0930-0325 : v205 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2012-9780817682866:ONLINE Show nearby items on shelf |
Title: | A Beginner's Guide to Discrete Mathematics [electronic resource] |
Author(s): |
W.D Wallis |
Date: | 2012 |
Edition: | Second Edition |
Publisher: | Boston : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Wallis's book on discrete mathematics is a resource for an introductory course in a subject fundamental to both mathematics and computer science, a course that is expected not only to cover certain specific topics but also tointroduce students to imp ortant modes of thought specific to each discipline . . . Lower-division undergraduates through graduate students. Choice (Review of the First Edition) Very appropriately entitled as a 'beginner's guide',this textbook presents itself as the first exposure to discrete mathematics and rigorous proof for the mathematics or computer science student. Zentralblatt MATH (Review of the First Edition) This second edition of A BeginnersGuide to Discrete Mathematicspresents a detailedguide to discrete mathematicsand its relationship to other mathematical subjects includingset theory, probability, cryptography, graph theory, and number theory.Thistextbookhas a distinctly applied orientation and explores a variety of applications. Key features of the second edition: * Includesa new chapter on the theory of voting as well asnumerous new examples and exercises throughout thebook * Introduces functions, vectors, matrices, number systems, scientific notations, and the representation of numbers in computers * Provides exam ples, which then lead into easy practice problems throughout the text, and fullexercises at the end of each chapter *Full solutions for practice problems are provided at the end of the book This text is intended for undergraduates in mathematics and compu ter science, however, featured special topics andapplications may also interest graduate students |
Note: | Springer eBooks |
Contents: | Properties of Numbers |
Sets and Data Structures | |
Boolean Algebras and Circuits | |
Relations and Functions | |
The Theory of Counting | |
Probability | |
Graph Theory | |
Matrices | |
Number Theory and Cryptography | |
The Theory of Voting | |
Solutions to Practic Exercises | |
Answers to Selected Exercises | |
Index | |
ISBN: | 9780817682866 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computational complexity , Combinatorics , Logic, Symbolic and mathematical , Mathematical statistics |
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Call number: | SPRINGER-2011-9783790827361:ONLINE Show nearby items on shelf |
Title: | Recent Advances in Functional Data Analysis and Related Topics [electronic resource] |
Author(s): |
Frdric Ferraty |
Date: | 2011 |
Publisher: | Heidelberg : Physica-Verlag HD |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | New technologies allow us to handle increasingly large datasets, while monitoring devices are becoming ever more sophisticated. This high-tech progress produces statistical units sampled over finer and finer grids. As themeasurement points become clo ser, the data can be considered as observations varying over a continuum. This intrinsic continuous data (called functional data) can be found in various fields of science, including biomechanics,chemometrics, econometrics, environmetrics, geophysics, med icine, etc. The failure of standard multivariate statistics to analyze such functional data has led the statistical community to develop appropriate statistical methodologies,called Functional Data Analysis (FDA). Today, FDA is certainly one of the most m otivating and popular statistical topics due to its impact on crucial societal issues (health, environment, etc). This is why the FDA statistical communityis rapidly growing, as are the statistical developments . Therefore, it is necessary to organize reg ular meetings in order to provide a state-of-art review of the recent advances in this fascinating area. This book collects selectedand extended papers presented at the second International Workshop of Functional and Operatorial Statistics (Santander, Spa in, 16-18 June, 2011), in which many outstanding experts on FDA will present the most relevant advances inthis pioneering statistical area. Undoubtedly, these proceedings will be an essential resource for academic researchers, master students, engineers, and practitioners not only in statistics but also in numerous related fields ofapplication. |
Note: | Springer eBooks |
Contents: | Penalized Spline Approaches for Functional Principal Component Logit |
Functional Prediction for the Residual Demand in Electricity Spot | |
Variable Selection in Semi | |
Functional RegressionModels | |
Power Analysis for Functional Change Point Detection | |
Robust Nonparametric Estimation for Functional Spatial Regression | |
Sequential Stability Procedures for Functional Data Setups | |
On the Effect of Noisy Observations of the Regressor in a Functional Linear Model | |
Testing the Equality of Covariance Operators | |
Modeling and Forecasting Monotone Curves by FDA | |
Wavelet | |
Based Minimum Contras | |
ISBN: | 9783790827361 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Contributions to Statistics, 1431-1968 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Gene expression , Meteorology , Computer vision , Distribution (Probability theory) |
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Call number: | SPRINGER-2011-9783642201929:ONLINE Show nearby items on shelf |
Title: | Statistics for High-Dimensional Data [electronic resource] : Methods, Theory and Applications |
Author(s): |
Peter Bhlmann Sara van de Geer |
Date: | 2011 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso andversions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory onhigh-dimensional statistics combined with methodolo gy, algorithms and illustrations with real data examples. This in-depth approach highlights the methods great potential and practical applicability in a variety of settings. Assuch, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science |
Note: | Springer eBooks |
Contents: | Introduction |
Lasso for linear models | |
Generalized linear models and the Lasso | |
The group Lasso | |
Additive models and many smooth univariate functions | |
Theory for the Lasso | |
Variable selection with the Lasso | |
Theory for l1/l2 | |
penalty procedures | |
Non | |
convex loss functions and l1 | |
regularization | |
Stable solutions | |
P | |
values for linear models and beyond | |
Boosting and greedy algorithms | |
Graphical modeling | |
Probability and moment inequalities | |
Author Index | |
Index | |
References | |
Problems at the end of each chapter | |
ISBN: | 9783642201929 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Statistics, 0172-7397 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Mathematical statistics |
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Call number: | SPRINGER-2011-9783642152023:ONLINE Show nearby items on shelf |
Title: | Mean Field Models for Spin Glasses [electronic resource] : Volume I: Basic Examples |
Author(s): |
Michel Talagrand |
Date: | 2011 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This is a new, completely revised, updated and enlarged edition of the author's Ergebnisse vol. 46: Spin Glasses: A Challenge for Mathematicians. This new edition will appear in two volumes, the present first volume presentsthe basic results and meth ods, the second volume is expected to appear in 2011. In the eighties, a group of theoretical physicists introduced several models for certain disordered systems, called spin glasses. These models aresimple and rather canonical random structures, of consi derable interest for several branches of science (statistical physics, neural networks and computer science). The physicists studied them by non-rigorous methods and predictedspectacular behaviors. This book introduces in a rigorous manner this exciting n ew area to the mathematically minded reader. It requires no knowledge whatsoever of any physics. The first volume of this new and completely rewrittenedition presents six fundamental models and the basic techniques to study them |
Note: | Springer eBooks |
Contents: | Introduction |
1. The Sherrington | |
Kirkpatrick Model | |
2. The Perceptron Model | |
3. The Shcherbina and Tirozzi Model | |
4. The Hopfield Model | |
5. The V | |
statistics Model | |
6. The Diluted SK Model and the K | |
Sat Problem | |
7. An Assignment Problem | |
A. Appendix: Elements of Probability Theory | |
References | |
Index | |
Glossary | |
ISBN: | 9783642152023 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics : v54 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Distribution (Probability theory) , Mathematical physics |
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Call number: | SPRINGER-2011-9783642133121:ONLINE Show nearby items on shelf |
Title: | Classification and Multivariate Analysis for Complex Data Structures [electronic resource] |
Author(s): |
Bernard Fichet Domenico Piccolo Rosanna Verde Maurizio Vichi |
Date: | 2011 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends,and to automatically b ag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first JointMeeting of the Socit Francophone de Classifi cation and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and theapplicative point of views, in the fields of Clustering, Classificatio n, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics |
Note: | Springer eBooks |
Contents: | Key Notes |
Classification and Discrimination | |
Data Mining | |
Robustness and Classification | |
Categorical Data and Latent Class Approach | |
Latent Variables and Related Methods | |
Symbolic, Multivalued and Conceptual Data Analysis | |
Spatial, Temporal, Streaming and Functional Data Analysis | |
Bio and Health Science | |
ISBN: | 9783642133121 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Studies in Classification, Data Analysis, and Knowledge Organization, 1431-8814 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Data structures (Computer science) , Computer science , Multimedia systems , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2011-9781441997821:ONLINE Show nearby items on shelf |
Title: | Targeted Learning [electronic resource] : Causal Inference for Observational and Experimental Data |
Author(s): |
Mark J van der Laan Sherri Rose |
Date: | 2011 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever data sets are massive, often measuring hundreds of thousands of measurements for a single subject.The fieldis ready to move towards c lear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjectivechoices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at bothstatisticians and applied researchers interested in causal inference and ge neral effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihoodestimator, including related concepts necessary to understand and apply these methods. Parts II-IX h andle complex data structures and topics applied researchers will immediately recognize from their own research, includingtime-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, a nd genomic studies. Targeted Learning, by Mark J. van der Laan and Sherri Rose, fills a much neededgap in statistical and causal inference. It protects us from wasting computational, analytical, and data resources on irrelevant aspects of a problem and te aches us how to focus on what is relevant answering questions thatresearchers truly care about. -Judea Pearl, Computer Science Department, University of California, Los Angeles In summary, this book should be on the shelf of every investigator who conduc ts observational research and randomizedcontrolled trials. The concepts and methodology are foundational for causal inference and at the same time stay true to what the data at hand can say a |
Note: | Springer eBooks |
Contents: | Models, Inference, and Truth |
The Open Problem | |
Defining the Model and Parameter | |
Super Learning | |
Introduction to TMLE | |
Understanding TMLE | |
Why TMLE? | |
Bounded Continuous Outcomes | |
Direct Effects and Effect Among the Treated | |
Marginal Structural Models | |
Positivity | |
Robust Analysis of RCTs Using Generalized Linear Models | |
Targeted ANCOVA Estimator in RCTs | |
Independent Case | |
Control Studies | |
Why Match? Matched Case | |
Control Studies | |
Nested Case | |
Control Risk Score Prediction | |
Super Learning for Right | |
Censored Data | |
RCTs with Time | |
to | |
Event Outcomes | |
RCTs with Time | |
ISBN: | 9781441997821 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Statistics, 0172-7397 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Mathematical statistics |
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Call number: | SPRINGER-2011-9781441996343:ONLINE Show nearby items on shelf |
Title: | Probability for Statistics and Machine Learning [electronic resource] : Fundamentals and Advanced Topics |
Author(s): |
Anirban DasGupta |
Date: | 2011 |
Publisher: | New York, NY : Springer New York : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in anextremely accessible sty le, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in itsunification of probability and statistics, its cov erage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year longgraduate course in statistics, computer science, or mathematics, for self-stud y, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory,asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processe s, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayesestimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probabilit y |
Note: | Springer eBooks |
Contents: | Chapter 1. Review of Univariate Probability |
Chapter 2. Multivariate Discrete Distributions | |
Chapter 3. Multidimensional Densities | |
Chapter 4. Advance Distribution Theory | |
Chapter 5. Multivariate Normal and Related Distributions | |
Chapter 6. Finite Sample Theory of Order Statistics and Extremes | |
Chapter 7. Essential Asymptotics and Applications | |
Chapter 8. Characteristic Functions and Applications | |
Chapter 9. Asymptotics of Extremes and Order Statistics | |
Chapter 10. Markov Chains and Applications | |
Chapter 11. Random Walks | |
Chapter 12. Brownian Motion and Gaussian Processes | |
ISBN: | 9781441996343 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer simulation , Bioinformatics , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2011-9781441994738:ONLINE Show nearby items on shelf |
Title: | An Introduction to Heavy-Tailed and Subexponential Distributions [electronic resource] |
Author(s): |
Sergey Foss Dmitry Korshunov Stan Zachary |
Date: | 2011 |
Edition: | 1 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Heavy-tailed probability distributions are an important component in the modelingof many stochastic systems. They are frequently used to accurately model inputs and outputs of computer and data networks and service facilitiessuch as call centers. The y are an essential for describingrisk processes in finance and also for insurance premia pricing, and such distributions occur naturally in models of epidemiological spread. The class includes distributionswith power law tails such asthe Pareto, as well a s the lognormal and certain Weibull distributions. This monograph defines the classes oflong-tailed and subexponential distributions in one dimension and provides a complete andcomprehensive description of their properties. New results are presented in a simple, coherent and systematic way. This leads to a comprehensive exposition of tail properties of sums of independent random variables whose distributionsbelong to the long-tailed and subexponential class. The book includes adiscussion of and references to contemporary areas of applications and also contains preliminary mathematicalmaterialwhich makes the book self contained.Modelers in the fields of finance, insurance, network science and environmental studies will find this book to be an essential ref erence |
Note: | Springer eBooks |
Contents: | Preface |
Introduction | |
Heavy | |
and long | |
tailed distributions | |
Subexponential distributions | |
Densities and local probabilities | |
Maximum of random walks | |
References | |
Index | |
ISBN: | 9781441994738 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Operations Research and Financial Engineering, 1431-8598 : v38 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Distribution (Probability theory) , Economics Statistics |
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Call number: | SPRINGER-2011-9781441917720:ONLINE Show nearby items on shelf |
Title: | Introduction to Modeling and Analysis of Stochastic Systems [electronic resource] |
Author(s): |
V. G Kulkarni |
Date: | 2011 |
Edition: | 2 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and publicpolicy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems. The book is devoted to the study ofimportant classes of stochastic processes: discre te and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematicallystudies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples, and case studies. The book provides a concise review of probability in the appendix.The book emphasizes numerical answers to the problems. A collection of MATLAB programs to acco mpany the this book can be downloaded from http://www.unc.edu/~vkulkarn/Maxim/maxim.zip. A graphical user interface to access the above filescan be downloaded from http://www.unc.edu/~vkulkarn/Maxim/maximgui.zip . The second edition incorporates several c hanges. First its title reflects the changes in content: the chapters on design and control have been removed. The booknow contains several case studies that teach the design principles. Two new chapters have been added. The new chapter on Poisson process es gives more attention to this important class of stochastic processes than the first edition did.The new chapter on Brownian motion reflects its increasing importance as an appropriate model for a variety of real-life situations, including finance. V. G . Kulkarni is Professor in the Department of Statistics and Operations Researchin the University of North Carolina, Chapel Hill. He has authored a graduate-level text Modeling and Analysis of Stochastic |
Note: | Springer eBooks |
Contents: | Introduction |
Discrete | |
Time Markov Models | |
Poisson Processes | |
Continuous | |
Time Markov Models | |
Generalized Markov Models | |
Queueing Models | |
Brownian Motion | |
ISBN: | 9781441917720 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Distribution (Probability theory) |
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Call number: | SPRINGER-2011-9780387775487:ONLINE Show nearby items on shelf |
Title: | Probability Measures on Semigroups [electronic resource] : Convolution Products, Random Walks and Random Matrices |
Author(s): |
Gran Hgns Arunava Mukherjea |
Date: | 2011 |
Edition: | 2 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Semigroups are very general structures and scientists often come across them in various contexts in science and engineering. In this second edition of Probability Measures on Semigroups, first published in the University Seriesin Mathematics in 1996, the authors present the theory of weak convergence of convolution products of probability measures on semigroups, the theory of random walks on semigroups, and their applications to products of random matrices.They examine the essentials of abstract semi group theory and its application to concrete semigroups of matrices. They present results on weak convergence, random walks, random matrices using semigroup ideas that for the most part arecomplete and best possible. Still, as the authors point out, there are other results that remain to be completed. These are all mentioned in the notes and comments at the end of each chapter, and will keep the readership of this bookenthusiastic and interested for some time to come. Apart from corrections of several err ors, new results have been added in the main text and in the appendices the references, all notes and comments at the end of each chapter havebeen updated, and exercises have been added. This volume is suitable for a one semester course on semigroups and it could be used as a main text or supplementary material for courses focusing on probability on algebraic structures orweak convergence. It is ideally suited to graduate students in mathematics, and in other fields such as engineering and sciences with a n interest in probability. Students in statistics using advance probability will also find ituseful. 'A well-written book...This is elegant mathematics, motivated by examples and presented in an accessible way that engages the reader.' International Stati stics Institute, December 1996 'This beautiful book...guides the readerthrough the most important developments...a valuable addition to the library of the probabilist, and a must for anybody interested |
Note: | Springer eBooks |
Contents: | Semigroups |
Probability Measures on Topological Semigroups | |
Random Walks on Semigroups | |
Random Matrices | |
Index | |
ISBN: | 9780387775487 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Probability and Its Applications, 1431-7028 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Topological Groups , Global analysis (Mathematics) , Distribution (Probability theory) |
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Call number: | SPRINGER-2010-9783790825985:ONLINE Show nearby items on shelf |
Title: | Recent Developments in Applied Probability and Statistics [electronic resource] : Dedicated to the Memory of Jrgen Lehn |
Author(s): |
Luc Devroye Blent Karaszen Michael Kohler Ralf Korn |
Date: | 2010 |
Publisher: | Heidelberg : Physica-Verlag HD |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book presents surveys on recent developments in applied probability and statistics. The contributions include topics such as nonparametric regression and density estimation, option pricing, probabilistic methods formultivariate interpolation, ro bust graphical modelling and stochastic differential equations. Due to its broad coverage of different topics the book offers an excellent overview of recent developments in applied probability andstatistics |
Note: | Springer eBooks |
Contents: | On Exact Simulation Algorithms for Some Distributions Related to Brownian Motion and Brownian Meanders |
A Review on Regression | |
based Monte Carlo Methods for Pricing American Options | |
Binomial Trees in Option Pricing | |
History, Practical Applications and Recent Developments | |
Uncertainty in Gaussian Process Interpolation | |
On the Inversive Pseudorandom Number Generator | |
Strong and Weak Approximation Methods for Stochastic Differential Equations Some Recent Developments | |
On Robust Gaussian Graphical Modelling | |
Strong Laws of Large Numbers and Nonparametric Estimation | |
Institute | |
ISBN: | 9783790825985 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2010-9783642121104:ONLINE Show nearby items on shelf |
Title: | Progress in Industrial Mathematics at ECMI 2008 [electronic resource] |
Author(s): |
Alistair D Fitt John Norbury Hilary Ockendon Eddie Wilson |
Date: | 2010 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This volume is the Proceedings of the European Conference on Mathematics for Industry held in London in June 2008. The aim of the meeting was to reinforce the role of mathematics as an overarching resource for industry andbusiness. Contributions cove r a wide range of mathematical techniques and the applications include manufacturing and technology, finance and policy-making, networks, medicine, and sport |
Note: | Springer eBooks |
Contents: | Part I |
Plenary Lectures | |
Part II Minisymposia | |
Part III Contributed Papers | |
ISBN: | 9783642121104 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics in Industry, 1612-3956 : v15 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differential equations, partial , Computer science Mathematics , Computer science , Numerical analysis , Distribution (Probability theory) , Economics Statistics |
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Call number: | SPRINGER-2010-9781441971654:ONLINE Show nearby items on shelf |
Title: | Applied Probability [electronic resource] |
Author(s): |
Kenneth Lange |
Date: | 2010 |
Edition: | Second |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
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Note: | Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. It can serve as a textbook for graduatestudents in applied ma thematics, biostatistics, computational biology, computer science, physics, and statistics. Readers should have a working knowledge of multivariate calculus, linear algebra, ordinary differential equations, andelementary probability theory. Chapter 1 revi ews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilisticapplications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented withappropriate sections from Chapters 1 and 2, there is sufficient material for a traditional se mester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, anddiffusion processes. The second edition adds two new chapters on asymptotic and numerical methods and an appendix that separates some of the more delicate mathematical theory from the steady flow of examples in the main text. Besidesthe two new chapters, the second edition includes a more extensive list of exercises, many additions to the exposition of combinatorics, new material on rates of convergence to equilibrium in reversible Markov chains, a discussion ofbasic reproduction numbers in population modeling, and better coverage of Brownian motion. Because many chapters are nearly self-contained, mathematical scientist s from a variety of backgrounds will find Applied Probability useful as areference. Kenneth Lange is the Rosenfeld Professor of Computational Genetics in the Departments of Biomathematics and Human Gene |
Note: | Springer eBooks |
Contents: | Basic Notions of Probability Theory |
Calculation of Expectations | |
Convexity, Optimization, and Inequalities | |
Combinatorics | |
Combinatorial Optimization | |
Poisson Processes | |
Discrete | |
Time Markov Chains | |
Continuous | |
Time Markov Chains | |
Branching Processes | |
Martingales | |
Diffusion Processes | |
Asymptotic Methods | |
Numerical Methods | |
Poisson Approximation | |
Number Theory | |
Appendix: Mathematical Review | |
ISBN: | 9781441971654 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X : v0 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Computer simulation , Biology Mathematics , Computer science Mathematics , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2010-9781441915764:ONLINE Show nearby items on shelf |
Title: | Introducing Monte Carlo Methods with R [electronic resource] |
Author(s): |
Christian Robert George Casella |
Date: | 2010 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better wayto develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view,explaining the R implementation of each simulatio n technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justificationof those methods has been considerably reduced, compared with Robert an d Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the Rprogramming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic randomgeneration algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters includeexercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation me thods but no previous exposure. It is meant to be useful for students and practitioners inareas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progres sively to be accessible to any reader. Christian P. Robert isProfessor of Statistics at Universit Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored |
Note: | Springer eBooks |
Contents: | Basic R Programming |
Random Variable Generation | |
Monte Carlo Integration | |
Controlling and Accelerating Convergence | |
Monte Carlo Optimization | |
MetropolisHastings Algorithms | |
Gibbs Samplers | |
Convergence Monitoring and Adaptation for MCMC Algorithms | |
ISBN: | 9781441915764 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Use R |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Computer simulation , Computer science Mathematics , Distribution (Probability theory) , Mathematical statistics , Engineering mathematics |
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Call number: | SPRINGER-2009-9783540893325:ONLINE Show nearby items on shelf |
Title: | Basics of Applied Stochastic Processes [electronic resource] |
Author(s): |
Richard Serfozo |
Date: | 2009 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerativeprocesses, Poisson proce sses, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of largenumbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerativeincrements. Extensive examples and exercises show how to formulate stochasti c models of systems as functions of a systems data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochasticnetworks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brown ian approximations, and varied Markovian models. The technical level of the volume is between that of introductory texts that focus onhighlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes. Intended r eaders are researchers and graduate students in mathematics, statistics, operations research, computer science,engineering, and business |
Note: | Springer eBooks |
Contents: | 1. Markov Chains |
2. Renewal and Regenerative Processes | |
3. Poisson Processes | |
4. Continuous | |
Time Markov Chains | |
5. Brownian Motion | |
6. Appendix | |
References | |
Notation | |
Index | |
ISBN: | 9783540893325 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Probability and Its Applications, 1431-7028 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Distribution (Probability theory) |
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Call number: | SPRINGER-2009-9781441901620:ONLINE Show nearby items on shelf |
Title: | An Intermediate Course in Probability [electronic resource] |
Author(s): |
Allan Gut |
Date: | 2009 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The purpose of this book is to provide the reader with a solid background and understanding of the basic results and methods in probability theory before entering into more advanced courses. The first six chapters focus on somecentral areas of what m ight be called pure probability theory: multivariate random variables, conditioning, transforms, order variables, the multivariate normal distribution, and convergence. A final chapter is devoted to the Poissonprocess as a means both to introduce stochast ic processes and to apply many of the techniques introduced earlier in the text. Students are assumed to have taken a first course in probability, though no knowledge of measure theory isassumed. Throughout, the presentation is thorough and includes many examples that are discussed in detail. Thus, students considering more advanced research in probability theory will benefit from this wide-ranging survey of thesubject that provides them with a foretaste of the subject's many treasures. The present second edition offers updated content, one hundred additional problems for solution, and a new chapter that provides an outlook on further areasand topics, such as stable distributions and domains of attraction, extreme value theory and records, and martingales . The main idea is that this chapter may serve as an appetizer to the more advanced theory. Allan Gut is Professor ofMathematical Statistics at Uppsala University, Uppsala, Sweden. He is a member of the International Statistical Institute, the Bernoulli S ociety, the Institute of Mathematical Statistics, and the Swedish Statistical Society. He is anAssociate Editor of the Journal of Statistical Planning and Inference and Sequential Analysis, a former Associate Editor of the Scandinavian Journal of Statisti cs, and the author of five other books including Probability: A GraduateCourse (Springer, 2005) and Stopped Random Walks: Limit Theorems and Applications, Second Edition (Springer, 2009) |
Note: | Springer eBooks |
Contents: | Multivariate Random Variables |
Conditioning | |
Transforms | |
Order Statistics | |
The Multivariate Normal Distribution | |
Convergence | |
An Outlook on Further Topics | |
The Poisson Process | |
ISBN: | 9781441901620 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Bioinformatics , Biology Mathematics , Distribution (Probability theory) , Mathematical statistics , Environmental sciences |
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Call number: | SPRINGER-2009-9780817648046:ONLINE Show nearby items on shelf |
Title: | Data Modeling for Metrology and Testing in Measurement Science [electronic resource] |
Author(s): |
Franco Pavese Alistair B Forbes |
Date: | 2009 |
Publisher: | Boston : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book and companion DVD provide a comprehensive set of modeling methods for data and uncertainty analysis, taking readers beyond mainstream methods described in standard texts. The emphasis throughout is on techniques having abroad range of real- world applications in measurement science. Mainstream methods of data modeling and analysis typically rely on certain assumptions that do not hold for many practical applications. Developed in this work are methodsand computational tools to address genera l models that arise in practice, allowing for a more valid treatment of calibration and test data and providing a deeper understanding of complex situations in measurement science. Additionalfeatures and topics of the book include: * Introduction to model ing principles in metrology and testing * Presentation of a basic probability framework in metrology and statistical approaches to uncertainty assessment * Discussion ofthe latest developments in data analysis using least squares, Fast Fourier Transform, wavelets, and fuzzy logic methods * Data fusion using neural networks, fuzzy methods, decision making, and risk analysis * A computer-assisted,rigorous approach to data evaluation and analysis of measurement software validity * Introduction to virtual ins truments, and an overview of IT tools for measurement science Data Modeling for Metrology and Testing in MeasurementScience may be used as a textbook in graduate courses on data modeling and computational methods, or as a training manual in the fields of calibration and testing. The book will also serve as an excellent reference for metrologists,mathematicians, statisticians, software engineers, chemists, and other practitioners with a general interest in measurement science |
Note: | Springer eBooks |
Contents: | Preface |
List of Contributors | |
An Introduction to Data Modelling Principles in Metrology and Testing | |
Probability in Metrology | |
Three Statistical Paradigms for the Assessment and Interpretation of Measurement Uncertainty | |
Interval Computations and Interval | |
Related Statistical Techniques: Tools for Estimating Uncertainty of the Results of Data Processing and Indirect Measurements | |
Parameter Estimation Based on Least Squares Methods | |
Frequency and TimeFrequency Domain Analysis Tools in Measurement | |
Data Fusion, Decision | |
Making, and Risk Analysis: Mathematical Tools and Techniq | |
ISBN: | 9780817648046 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Modeling and Simulation in Science, Engineering and Technology |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science Mathematics , Distribution (Probability theory) , Mathematical statistics , Statistics , Industrial engineering |
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Call number: | SPRINGER-2009-9780817647490:ONLINE Show nearby items on shelf |
Title: | Scan Statistics [electronic resource] : Methods and Applications |
Author(s): |
Joseph Glaz Vladimir Pozdnyakov Sylvan Wallenstein |
Date: | 2009 |
Publisher: | Boston, MA : Birkhuser Boston |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Scan statistics is currently one of the most active and important areas of research in applied probability and statistics, having applications to a wide variety of fields: archaeology, astronomy, bioinformatics, biosurveillance,molecular biology, gen etics, computer science, electrical engineering, geography, material sciences, physics, reconnaissance, reliability and quality control, telecommunication, and epidemiology. Filling a gap in the literature, thisself-contained volume brings together a coll ection of selected chapters illustrating the depth and diversity of theory, methods and applications in the area of scan statistics. Key features: * Chapters are written by leading experts inthe field. * Features many current results and highlights new di rections for future research. * Includes challenging theoretical methodological research problems. * Presentation is accessible to statisticians as well as to scientistsfrom other disciplines where scan statistics are employed. * Real-world applications t o areas such as bioinformatics and biosurveillance are emphasized. * Contains extensive references to research articles, books, and relevant computersoftware. Scan Statistics is an excellent reference for graduate students and researchers in applied proba bility and statistics, as well as for scientists in biology, computer science, pharmaceutical science, medicine, geography,quality control, communications, and epidemiology. The work may also be used as a textbook for a graduate-level seminar on scan stat istics |
Note: | Springer eBooks |
Contents: | Joseph Naus: Father of the Scan Statistic |
Precedence | |
Type Tests for the Comparison of Treatments with a Control | |
Extreme Value Results for Scan Statistics | |
Boundary Crossing Probability Computationsin the Analysis of Scan Statistics | |
Approximations for Two | |
Dimensional Variable Window Scan Statistics | |
Applications of Spatial Scan Statistics: A Review | |
Extensions of the Scan Statistic for the Detection and Inference of SpatialClusters | |
1 | |
Dependent Stationary Sequences and Applications to Scan Statistics | |
Scan Statistics in Genome | |
Wide Scan for Complex Trait Loci | |
On Probabilit | |
ISBN: | 9780817647490 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Statistics for Industry and Technology |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Mathematics , Physiology Mathematics , Distribution (Probability theory) , Mathematical statistics |
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Call number: | SPRINGER-2009-9780387924076:ONLINE Show nearby items on shelf |
Title: | A First Course in Bayesian Statistical Methods [electronic resource] |
Author(s): |
Peter D Hoff |
Date: | 2009 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advancedreaders to quickly grasp th e principles underlying Bayesian theory and methods. The examples and computer code allow the reader to understand and implement basic Bayesian data analyses using standard statistical models and to extendthe standard models to specialized data analysis s ituations. The book begins with fundamental notions such as probability, exchangeability and Bayes' rule, and ends with modern topics such as variable selection in regression,generalized linear mixed effects models, and semiparametric copula estimation. N umerous examples from the social, biological and physical sciences show how to implement these methodologies in practice. Monte Carlo summaries ofposterior distributions play an important role in Bayesian data analysis. The open-source R statistical compu ting environment provides sufficient functionality to make Monte Carlo estimation very easy for a large number of statisticalmodels and example R-code is provided throughout the text. Much of the example code can be run ``as is'' in R, and essentially all of it can be run after downloading the relevant datasets from the companion website for this book. PeterHoff is an Associate Professor of Statistics and Biostatistics at the University of Washington. He has developed a variety of Bayesian methods for mul tivariate data, including covariance and copula estimation, cluster analysis, mixturemodeling and social network analysis. He is on the editorial board of the Annals of Applied Statistics |
Note: | Springer eBooks |
Contents: | Introduction and examples |
Belief, probability and exchangeability | |
One parameter models | |
Monte Carlo approximation | |
The normal model | |
Posterior approximation with the Gibbs sampler | |
The multivariate normal model | |
Group comparisons and hierarchical modeling | |
Linear regression | |
Nonconjugate priors and the Metropolis | |
Hastings algorithm | |
Linear and generalized linear mixed effects models | |
Latent variable methods for ordinal data | |
ISBN: | 9780387924076 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Texts in Statistics, 1431-875X |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Data mining , Mathematical statistics , Econometrics , Social sciences Methodology |
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Call number: | SPRINGER-2009-9780387790527:ONLINE Show nearby items on shelf |
Title: | Introduction to Nonparametric Estimation [electronic resource] |
Author(s): |
Alexandre B Tsybakov |
Date: | 2009 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | Methods of nonparametric estimation are located at the core of modern statistical science. The aim of this book is to give a short but mathematically self-contained introduction to the theory of nonparametric estimation. Theemphasis is on the constru ction of optimal estimators therefore the concepts of minimax optimality and adaptivity, as well as the oracle approach, occupy the central place in the book. This is a concise text developed from lecturenotes and ready to be used for a course on the grad uate level. The main idea is to introduce the fundamental concepts of the theory while maintaining the exposition suitable for a first approach in the field. Therefore, the results arenot always given in the most general form but rather under assumptions that lead to shorter or more elegant proofs. The book has three chapters. Chapter 1 presents basic nonparametric regression and density estimators and analyzestheir properties. Chapter 2 is devoted to a detailed treatment of minimax lower bounds. Chapter 3 develops more advanced topics: Pinsker's theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity |
Note: | Springer eBooks |
Contents: | Nonparametric estimators |
Lower bounds on the minimax risk | |
Asymptotic efficiency and adaptation | |
Appendix | |
References | |
Index | |
ISBN: | 9780387790527 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Statistics, 0172-7397 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Optical pattern recognition , Distribution (Probability theory) , Mathematical statistics , Econometrics |
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Call number: | SPRINGER-2009-9780387768526:ONLINE Show nearby items on shelf |
Title: | Generalized Measure Theory [electronic resource] |
Author(s): |
Zhenyuan Wang George J Klir |
Date: | 2009 |
Publisher: | Boston, MA : Springer US |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This comprehensive text examines the relatively new mathematical area of generalized measure theory. This area expands classical measure theory by abandoning the requirement of additivity and replacing it with various weakerrequirements. Each of thes e weaker requirements characterizes a class of nonadditive measures. This results in new concepts and methods that allow us to deal with many problems in a more realistic way. For example, it allows us to workwith imprecise probabilities. The exposition o f generalized measure theory unfolds systematically. It begins with preliminaries and new concepts, followed by a detailed treatment of important new results regarding various types ofnonadditive measures and the associated integration theory. The latter involves several types of integrals: Sugeno integrals, Choquet integrals, pan-integrals, and lower and upper integrals. All of the topics are motivated by numerousexamples, culminating in a final chapter on applications of generalized measure theory. Some key features of the book include: many exercises at the end of each chapter along with relevant historical and bibliographical notes, anextensive bibliography, and name and subject indices. The work is suitable for a classroom setting at the graduate lev el in courses or seminars in applied mathematics, computer science, engineering, and some areas of science. A soundbackground in mathematical analysis is required. Since the book contains many original results by the authors, it will also appeal to resear chers working in the emerging area of generalized measure theory. About the Authors: ZhenyuanWang is currently a Professor in the Department of Mathematics of University of Nebraska at Omaha. His research interests have been in the areas of nonadditive me asures, nonlinear integrals, probability and statistics, and data mining.He has published one book and many papers in these areas. George J. Klir is currently a Distinguished Professor of Systems Scienc |
Note: | Springer eBooks |
Contents: | Preface |
Introduction | |
Preliminaries | |
Basic Ideas of Generalized Measure Theory | |
Special Area of Generalized Measure Theory | |
Extensions | |
Structural Characteristics | |
Measurable Functions on Monotone Measure Space | |
Integration | |
Sugeno Integrals | |
Pan | |
Intergrals | |
Choquet Integral | |
Upper and Lower Integrations | |
Constructing Generalized Measures | |
Fuzzification in Generalized Measure Theory | |
Applications of Generalized Measure Theory | |
Bibliography | |
Appendix A. Glossary of Key Concepts | |
Appendix B. Glossary of Symbols | |
Name Index | |
Subject Index | |
ISBN: | 9780387768526 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | IFSR International Series on Systems Science and Engineering, 1574-0463 : v25 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Systems theory , Logic, Symbolic and mathematical |
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Call number: | SPRINGER-2009-9780387709840:ONLINE Show nearby items on shelf |
Title: | Mathematical Biology [electronic resource] : An Introduction with Maple and Matlab |
Author(s): |
Ronald W Shonkwiler James Herod |
Date: | 2009 |
Publisher: | New York, NY : Springer New York |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This text presents mathematical biology as a field with a unity of its own, rather than only the intrusion of one science into another. It updates an earlier successful edition and greatly expands the concept of the computerbiology laboratory, giving students a general perspective of the field before proceeding to more specialized topics. The book focuses on problems of contemporary interest, such as cancer, genetics, and the rapidly growing field ofgenomics. It includes new chapters on parasites, ca ncer, and phylogenetics, along with an introduction to online resources for DNA, protein lookups, and popular pattern matching tools such as BLAST. In addition, the emerging field ofalgebraic statistics is introduced and its power illustrated in the conte xt of phylogenetics. A unique feature of the book is the integration of a computer algebra system into the flow of ideas in a supporting but unobtrusive role.Syntax for both the Maple and Matlab systems is provided in a tandem format. The use of a compute r algebra system gives the students the opportunity to examine what if scenarios, allowing them to investigate biological systems in away never before possible. For students without access to Maple or Matlab, each topic presented is complete. Graphic visu alizations are provided for all mathematical results. Mathematical Biology includes extensive exercises, problemsand examples. A year of calculus with linear algebra is required to understand the material presented. The biology presented proceeds from the study of populations down to the molecular level no previous coursework in biology isnecessary. The book is appropriate for undergraduate and graduate students studying mathematics or biology and for scientists and researchers who wish to study the appli cations of mathematics and computers in the natural sciences |
Note: | Springer eBooks |
Contents: | Biology, Mathematics and a Mathematical Biology Laboratory |
Some Mathematical Tools | |
Reproduction of the Drive for Survival | |
Interactions Between Organisms and Their Environment | |
Age | |
Dependent Population Structures | |
Random Movements in Space and Time | |
The Biological Disposition of Drugs and Inorganic Toxins | |
Neurophysiology | |
The Biochemistry of Cells | |
The Biological Disposition of Drugs and Inorganic Toxins | |
A Biomathematical Approach to HIV and AIDS | |
Parasites and Their Diseases | |
Genetics | |
Genomics | |
Phylogenetics | |
ISBN: | 9780387709840 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Undergraduate Texts in Mathematics, 0172-6056 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Computer science , Biology Data processing , Biology Mathematics , Distribution (Probability theory) |
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Call number: | SPRINGER-2008-9783790820645:ONLINE Show nearby items on shelf |
Title: | Recent Advances in Linear Models and Related Areas [electronic resource] : Essays in Honour of Helge Toutenburg |
Author(s): |
Shalabh Christian Heumann |
Date: | 2008 |
Publisher: | Heidelberg : Physica-Verlag HD |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | The theory of linear models and regression analysis plays an essential role in the development of methods for the statistical modelling of data. The book presents the most recent developments in the theory and applications oflinear models and related areas of active research. The contributions include topics such as boosting, Cox regression models, cluster analysis, design of experiments, feasible generalized least squares, information theory, matrixtheory, measurement error models, missing data mode ls, mixture models, panel data models, penalized least squares, prediction, regression calibration, spatial models and time series models. Several contributions illustrate applicationsin biomedical research, economics, finance, genetic epidemiology and me dicine |
Note: | Springer eBooks |
ISBN: | 9783790820645 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Distribution (Probability theory) , Mathematical statistics , Economics, Mathematical |
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Call number: | SPRINGER-2008-9783540742272:ONLINE Show nearby items on shelf |
Title: | Linear Models and Generalizations [electronic resource] : Least Squares and Alternatives |
Author(s): |
C. Radhakrishna Rao Shalabh Helge Toutenburg Christian Heumann |
Date: | 2008 |
Edition: | Third Extended Edition |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linearmodels play a part. Th e authors present a unified theory of inference from linear models and its generalizations with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation andtesting based on convex loss functions and gene ral estimating equations. Some of the highlights include: sensitivity analysis and model selection, analysis of incomplete data, analysis of categorical data based on a unifiedpresentation of generalized linear models including GEE- and full likelihood me thods for correlated response, an extensive appendix on matrix theory, useful to researchers in econometrics, engineering and optimization theory. For thisthird edition the text has been extensively revised and contains the latest developments in the area of linear models |
Note: | Springer eBooks |
Contents: | 1. Introduction |
2. The Simple Linear Regression Model | |
3. The Multiple Linear Regression Model | |
4. The Generalized Linear Regression Model | |
5. Exact and Stochastic Linear Restrictions | |
6. Prediction Problems in the Generalized Regression Model | |
7. Sensitivity Analysis | |
8. Analysis of Incomplete Data Sets | |
9. Robust Regression | |
10. Models for Categorical Response Variables | |
Fitting Smooth Functions | |
Appendix A: Matrix Algebra | |
ISBN: | 9783540742272 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Springer Series in Statistics, 0172-7397 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Statistics , Computer science , Distribution (Probability theory) , Mathematical statistics , Economics, Mathematical |
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Call number: | SPRINGER-2008-9783540719922:ONLINE Show nearby items on shelf |
Title: | Progress in Industrial Mathematics at ECMI 2006 [electronic resource] |
Author(s): |
Luis L Bonilla Miguel Moscoso Gloria Platero Jose M Vega |
Date: | 2008 |
Publisher: | Berlin, Heidelberg : Springer Berlin Heidelberg |
Size: | 1 online resource |
Note: | Springer e-book platform |
Note: | Springer 2013 e-book collections |
Note: | ECMI is synonymous with European Mathematics for Industry and organizes successful biannual conferences. The 14th European Conference for Mathematics in Industry held in Legans (Madrid) focused on Aerospace, Information andCommunications, Materials, Energy and Environment, Imaging, Biology and Biotechnology, Life Sciences, Finances and other topics including Education in Industrial Mathematics and web learning. Attendees came from all over the world.Overall, these proceedings give a lively overview o f the importance of mathematical modeling, analysis and numerical methods when addressing and solving problems from todays real world applications. The accessible presentation ofreal problems from industry and finance, modeling, solutions via appropriate numerical and mathematical techniques are a source of fresh ideas and inspiration for mathematicians. Engineers and scientists in application fields may finduseful ideas and techniques presented in familiar contexts that may help them to solve related pro blems in industry. Educators may find discussions of novel teaching experiences and examples from industrial contexts that could beuseful devising curricula which include industrial mathematics and web learning |
Note: | Springer eBooks |
Contents: | Plenary Lectures |
Minisymposia | |
Contributed Papers | |
ISBN: | 9783540719922 |
Series: | e-books |
Series: | SpringerLink (Online service) |
Series: | Mathematics in Industry, The European Consortium for Mathematics in Industry, 1612-3956 : v12 |
Series: | Mathematics and Statistics (Springer-11649) |
Keywords: | Mathematics , Differential equations, partial , Computer science Mathematics , Computer science , Numerical analysis , Distribution (Probability theory) , Economics Statistics |
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