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 evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. This book is suitable for statisticians seeking to learn more about application in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Table Of Contents: Markov Models in molecular evolution?Nicolas Galtier, Olivier Gascuel, Alain Jean-Marie. Introduction to Applications of the Likelihood Function in Molecular Evolution?Arndt von Haeseler. Introduction to Markov Chain Monte Carlo Methods in Molecular Evolution?Bret Larget. Population Genetics of Molecular Evolution?Carlos D. Bustamante. Maximum Likelihood Methods for Detecting Adaptive Protein Evolution?Joseph P. Bielawski and Ziheng Yang. HyPhy: Hypothesis Testing Using Phylogenies?Sergei L. Kosakovsky Pond and Spencer V. Muse. Bayesian Analysis of Molecular Evolution using Mr.Bayes?John P. Huelsenbeck and Fredrik Ronquist. Est
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