title: Bayesian phylogenetic inference via Markov chain Monte Carlo methods.
abstract: 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.
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, 53706-1685, USA. Robertm@genetics.wisc.edu
authors.forename: B
authors.initials: B
authors.lastname: Mau
authors.forename: M A
authors.initials: MA
authors.lastname: Newton
authors.forename: B
authors.initials: B
authors.lastname: Larget
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D004247:DNA / Q000235:genetics; D008390:Markov Chains; D009010:Monte Carlo Method; D010471:Perches / Q000235:genetics; D010802:Phylogeny; D010944:Plants / Q000235:genetics; D013269:Stochastic Processes
chemical_list: D004247:DNA