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.

Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical 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 an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they 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 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.

Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed

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 MrBayes - John P. Huelsenbeck and Fredrik Ronquist.- Estimation of divergence times from molecular sequence data - Jeffrey L. Thorne, Hirohisa Kishino.- Markov Models of Protein Sequence Evolution - Matthew W. Dimmic.- Models of Microsatellite Evolution - Peter Calabrese, Raazesh Sainudiin.- Genome Rearrangement - Rick Durrett.- Phylogenetic Hidden Markov Models - Adam Siepel, David Haussler.- The Evolutionary Causes and Consequences of Base Composition Variation - Gilean A.T. McVean.- Statistical Alignment: Recent Progress, New Applications, and Challenges - Gerton Lunter, Alexei J. Drummond, Istvan Miklos and Jotun Hein.- Estimating Substitution Matrices - Von Bing Yap, Terry Speed.- Posterior Mapping and Posterior Predictive Distributions - Jonathan P. Bollback.- Assessing the Uncertainty in Phylogenetic Inference - Hidetoshi Shimodaira, Masami Hasegawa.