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SPIRES-BOOKS: FIND KEYWORD PLANT GENETICS AND GENOMICS *END*INIT* use /tmp/qspiwww.webspi1/4973.14 QRY 131.225.70.96 . find keyword "plant genetics and genomics" ( in books using www Cover
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Call number:SPRINGER-2008-9780387759654:ONLINE Show nearby items on shelf
Title:Statistical Design [electronic resource]
Author(s): George Casella
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriatedesign, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive gooddesign, paying attention to both the theoretical b ackground and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block,to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspectsof statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintainsactive research interests in the theory and application of Monte Carlo and other computationally intensive metho ds. He is listed as an ISI Highly Cited Researcher. In other capacities, Professor Casella has served as Theory andMethods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co -Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served onthe Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institut e of Mathematical Statistics. Professor Casella has co-authored fivetextbooks: Variance Components, 1992 Theory of Point Estimation, Second Edition, 1998 Monte Carlo Statistical Methods, Second Edit
Note:Springer eBooks
Contents:Basics
Completely randomized designs
Complete block designs
Interlude: assessing the effects of blocking
Split plot designs
Confounding in blocks
ISBN:9780387759654
Series:e-books
Series:SpringerLink (Online service)
Series:Springer Texts in Statistics, 1431-875X
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Human genetics , Plant breeding , Morphology (Animals) , Mathematical statistics
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Call number:SPRINGER-2005-9780387277332:ONLINE Show nearby items on shelf
Title:Statistical Methods in Molecular Evolution [electronic resource]
Author(s): Rasmus Nielsen
Date:2005
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolutionhas become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advancedcomputational methods including high-dimensional n umerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes anintroductory section suitable for readers that are new to the field, a section d iscussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating toestimation and model choice. The chapters are written by the leaders in the field and they will take the r eader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticiansseeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning m ore about the theory behind the statistical methods applied in the field. The chapters of the bookassume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statisti cal concepts are introduced in the book in the context of their application inmolecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 andafter a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rmer
Note:Springer eBooks
Contents:Markov Models in Molecular Evolution
to Applications of the Likelihood Function in Molecular Evolution
to Markov Chain Monte Carlo Methods in Molecular Evolution
Population Genetics of Molecular Evolution
Practical Approaches for Data Analysis
Maximum Likelihood Methods for Detecting Adaptive Protein Evolution
HyPhy: Hypothesis Testing Using Phylogenies
Bayesian Analysis of Molecular Evolution Using MrBayes
Estimation of Divergence Times from Molecular Sequence Data
Models of Molecular Evolution
Markov Models of Protein Sequence Evolution
Models of Microsatelli
ISBN:9780387277332
Series:e-books
Series:SpringerLink (Online service)
Series:Statistics for Biology and Health, 1431-8776
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Bioinformatics , Evolution (Biology) , Plant breeding , Genetics Mathematics , Biology Mathematics
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Call number:SPRINGER-2002-9780387227641:ONLINE Show nearby items on shelf
Title:Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
Author(s): Daniel Sorensen
Date:2002
Size:1 online resource (740 p.)
Note:10.1007/b98952
Contents:Review of Probability and Distribution Theory -- Uncertainty, Random Variables, and Probability Distributions -- Uncertainty about Functions of Random Variables -- Methods of Inference -- An Introduction to Likelihood Inference --
Further Topics in Likelihood Inference -- An Introduction to Bayesian Inference -- Bayesian Analysis of Linear Models -- The Prior Distribution and Bayesian Analysis -- Bayesian Assessment of Hypotheses and Models -- Approximate
Inference Via the EM Algorithm -- Markov Chain Monte Carlo Methods -- An Overview of Discrete Markov Chains -- Markov Chain Monte Carlo -- Implementation and Analysis of MCMC Samples -- Applications in Quantitative Genetics -- Gaussian
and Thick-Tailed Linear Models -- Threshold Models for Categorical Responses -- Bayesian Analysis of Longitudinal Data -- to Segregation and Quantitative Trait Loci Analysis
ISBN:9780387227641
Series:eBooks
Series:SpringerLink (Online service)
Series:Springer eBooks
Keywords: Life sciences , Biochemistry , Plant genetics , Animal genetics , Statistics , Life Sciences , Biochemistry, general , Statistics for Life Sciences, Medicine, Health Sciences , Animal Genetics and Genomics , Plant Genetics & Genomics
Availability:Click here to see Library holdings or inquire at Circ Desk (x3401)
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