Molecular Evolution: A Statistical Approach - Tapa dura

Molecular Evolution: A Statistical Approach is a significant update of Ziheng Yangs previous book Computational Molecular Evolution, which was published in 2006 ... Yang has produced a book that could be readand enjoyed by different audiences for different purposes. I think that the book will be welcomed by biologists who want some mathematical intuition for whats underneath the hood of the methods they rely on. Yang has made a serious effort to keep the book understandable to non-mathematicians, and as a result the book does not feel overly mathematical or dense with notation despite the fact that it covers a lot of mathematical terrain. (Barbara R. Holland, Systematic Biology)

I think Molecular Evolution: A Statistical Approach would also work very well as a text for a graduate level course in statistical phylogenetics ... The exercises at the end of each chapterwould be useful for academics anting to use the book as a course textquestions cover an interesting range of problems that would get the class both thinking and programming. (Barbara R. Holland, Systematic Biology)

Written by an expert in the field, the book emphasizes conceptual understanding ... aimed at graduate level students and professional researchers (Biotechnology, Agronomy, Society and Environment)

Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation.

Molecular Evolution: a statistical approach presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. Written by an expert in the field, the book emphasizes conceptual understanding rather than mathematical proofs. The text is enlivened with numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative.

This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.