Bayesian Phylogenetics

Ming-Hui Chen is a professor of statistics and director of the Statistical Consulting Services at the University of Connecticut. He was the recipient of the 2013 American Association of the University Professors Research Excellence Award, the 2013 College of Liberal Arts and Sciences Excellence in Research Award in the Physical Sciences Division at the University of Connecticut, and the 2011 International Chinese Statisticians Association (ICSA) Outstanding Service Award. An elected fellow of the ASA and the IMS, Dr. Chen has served on numerous professional committees, including the 2013 president of the ICSA, the 2011-2013 board of directors of the International Society for Bayesian Analysis, the 2007-2010 executive director of the ICSA, and the 2004-2006 board of directors of the ICSA. He has also served on editorial boards of Bayesian Analysis, Journal of the American Statistical Association, Journal of Computational and Graphical Statistics, Lifetime Data Analysis, Sankhya, and Statistics and Its Interface. His research interests include Bayesian statistical methodology, Bayesian computation, Bayesian phylogenetics, categorical data analysis, design of Bayesian clinical trials, DNA microarray data analysis, meta-analysis, missing data analysis, Monte Carlo methodology, prior elicitation, statistical methodology and analysis for prostate cancer data, and survival data analysis.Lynn Kuo is a professor of statistics at the University of Connecticut. An elected fellow of the ASA, she was previously a research fellow in the Statistical Survey Institute at the USDA and at the Statistical and Applied Mathematical Sciences Institute (SAMSI). Dr. Kuo received an outstanding service award from ICSA in 2013 and was the secretary and treasurer of the Section of Bayesian Statistics of the ASA in 1998-1999. She has been an associate editor of the Journal of American Statistical

"… a great resource to get up to speed with the current state and future directions of many areas of research in Bayesian phylogenetics. I expect Bayesian Phylogenetics will be an important resource as Bayesian approaches to phylogenetics continue to advance and diversify. It introduces many novel methods and proofs that will be of broad interest to statisticians, and includes thorough reviews of several exciting areas of statistical phylogenetics. As a result, many of the book’s chapters will be important (and likely highly cited) references."—Systematic Biology, August 2015"… the first book devoted solely to Bayesian methods in this field … . The affiliations of the editors of the volume reflect the strong interdisciplinary flavor of the book … . The diversity of contributing authors further reinforces this focus, with authors spanning the range from statisticians to phylogeneticists. … the volume does achieve the editors’ primary goal of bringing state-of-the-art developments at the intersection of Bayesian methodology and phylogenetic inference to the forefront in a manner in which they can be appreciated by researchers in both fields."—Journal of the American Statistical Association, June 2015"Bayesian Phylogenetics has the distinction of being the first published text devoted solely to the Bayesian approach to phylogenetics. … a useful resource for many researchers in the field and for statisticians interested in joining the game."—International Statistical Review, 2015"This book provides an extensive and concrete account of modern Bayesian phylogenetics … a good starting point for entering each subarea of Bayesian phylogenetics. … this book can serve as a road map for both starters and those who are already in this field. … The book is concise, and the examples accompanying each topic are clear and supported by relevant illustrations. … useful to researchers and graduate students."—Biometrics, March 2015

• Bayesian phylogenetics: methods, computational algorithms, and applications. Priors in Bayesian phylogenetics. IDR for marginal likelihood in Bayesian phylogenetics. Bayesian model selection in phylogenetics and genealogy-based population genetics. Variable tree topology stepping-stone marginal likelihood estimation. Consistency of marginal likelihood estimation when topology varies. Bayesian phylogeny analysis. Sequential Monte Carlo (SMC) for Bayesian phylogenetics. Population model comparison using multi-locus datasets. Bayesian methods in the presence of recombination. Bayesian nonparametric phylodynamics. Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution. Bayesian inference of species divergence times. Index.