Provides a comprehensive review of the many interesting statistical problems arising in molecular evolution provided by the leading researchers in this field. For the statistician the book provides an introduction to an exciting area of application that often has been overlooked by statisticians. For the biologist the book provides an introduction to the theory underlying many of the methods they use in their daily research.
From the reviews: "...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006 "Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006 "Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006 "This book has managed a rare thing: to become more than the sum of its parts. It provides an introduction to a lot of the most exciting research going on in molecular evolution, describing it from different perspectives and providing readers with a key to explore different aspects of the literature. ! Statistical Methods in Molecular Evolution is a strong book ! and it should rank highly on Systematic Biology readers' 'to buy' lists." (Simon Whelan, Systematic Biology, Vol. 55 (4), 2006) "The past few decades have seen a vigorous development ! in the field of molecular evolution. ! the number of publications in journals at the interface of evolution, genetics, bioinformatics, statistics and probability is steadily increasing. A text which presents a survey in this very interface is therefore highly welcome. ! I like the book and recommend it warmly. ! The book is a quite lucid and timely survey about probabilistic models and statistical methods in a fascinating field of modern biology." (Anton Wakolbinger, Metrika, Vol. 64, 2006) "This book provides an overview of the statistical theory and methods used in studies of molecular evolution. ! The chapters are written by the leaders in the field and will take the reader from basic introductory material to the state-of-the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists ! . the book should be accessible for most biology graduate students ! ." (T. Postelnicu, Zentralblatt MATH, Vol. 1093 (19), 2006)
Markov models in molecular evolution.- Introduction to Applications of the Likelihood Function in Molecular Evolution.- Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution.- Population Genetics of Molecular Evolution.- 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.- Markov Models of Protein Sequence Evolution.- Models of Microsatellite Evolution.- Genome Rearrangement.- Phylogenetic Hidden Markov Models.- The Evolutionary Causes and Consequences of Base Composition Variation.- Statistical Alignment: Recent Progress, New Applications, and Challenges.- Estimating Substitution Matrices.- Posterior Mapping and Posterior Predictive Distributions.- Assessing the Uncertainty in Phylogenetic Inference.
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