Bayesian phylogenetic inference via Markov chain Monte Carlo methods
Bob Mau, Michael Newton, and Bret Larget
Duquesne University
Department of Mathematics and Computer Science
Technical Report #97-03, November 1997

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 nine plant species, then extend to DNA sequences from thirty-two 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.

Article (PostScript)

Department Technical Reports

Maintained by
Revised .