Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods
1999/03/01 by Bob Mau, Michael A. Newton, Bret Larget · 549 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Mathematics · #Algorithm #Bayesian inference #Bayesian probability #Biology #Combinatorics #Computer science #Evolution and Paleontology Studies #Genetic diversity and population structure #Genomics and Phylogenetic Studies #Markov chain #Markov chain Monte Carlo #Mathematics #Phylogenetic tree #Posterior probability #Statistics #Tree (set theory)
paper · pdf · doi:10.1111/j.0006-341x.1999.00001.x
published in Biometrics 55(1), 1-12 (Oxford University Press)
openalex publication_date 1999/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.
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