Bayesian evolutionary analysis with BEAST
What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: " Addresses the theoretical aspects of the fiel…
Read More ↓Title Statements
- Full title :Bayesian evolutionary analysis with BEAST / Alexei J. Drummond, University of Auckland, New Zealand, Remco R. Bouckaert, University of Auckland, New Zealand.
Summary
What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: " Addresses the theoretical aspects of the field " Advises on how to prepare and perform phylogenetic analysis " Helps with interpreting analyses and visualisation of phylogenies " Describes the software architecture " Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users - from those using phylogenetic tools, to computational biologists and Bayesian statisticians
Read More ↓Edition Statements
- Publication Date:2015.
Publication Statements
- Publisher(s):Cambridge University Press
- Place of Publication:Cambridge, United Kingdom
- Language:eng
- Publication Date:2015.
The Physical Object
- Description:1 online resource (xii, 249 pages .)
Edition Identifiers
- ISBN:9781139095112, 1139095110, 1316308480, 9781316308486
- oclc:ocn966394950
- LC Classification:QH83
- Dewey Decimal:578.01/2
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