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Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods
Author(s) -
Mau Bob,
Newton Michael A.,
Larget Bret
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00001.x
Subject(s) - markov chain monte carlo , markov chain , phylogenetic tree , tree (set theory) , mathematics , posterior probability , bayesian inference , algorithm , computer science , bayesian probability , statistics , combinatorics , biology , gene , biochemistry
Summary. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees close to the current tree in the chain. We illustrate the algorithm with restriction site data on 9 plant species, then extend to DNA sequences from 32 species of fish. The algorithm mixes well in both examples from random starting trees, generating reproducible estimates and credible sets for the path of evolution.