Molecular Evolution: A Statistical Approach

ISBN-13: 9780199602612

ISBN-10: 0199602611

Author: Ziheng Yang

Edition: 1

Publication date:
2014
Publisher:
Oxford University Press
Format:
Paperback 512 pages
Category:
Chemistry, Zoology, Archaeology
Get cash immediately!
SELL
Buy or Rent
On Amazon
from $45.99
FREE shipping on ALL orders

Summary

Acknowledged author Ziheng Yang wrote Molecular Evolution: A Statistical Approach comprising 512 pages back in 2014. Textbook and etextbook are published under ISBN 0199602611 and 9780199602612. Since then Molecular Evolution: A Statistical Approach textbook was available to sell back to BooksRun online for the top buyback price of $6.41 or rent at the marketplace.


Description

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.