Abstract
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.
Keywords
Markov chain Monte CarloMarkov chainPhylogenetic treeTree (set theory)MathematicsPosterior probabilityBayesian inferenceAlgorithmComputer scienceBayesian probabilityStatisticsCombinatoricsBiology
Affiliated Institutions
Related Publications
CODA: convergence diagnosis and output analysis for MCMC
[1st paragraph] At first sight, Bayesian inference with Markov Chain Monte Carlo (MCMC) appears to be straightforward. The user defines a full probability model, perhaps using o...
Comparative Performance of Bayesian and AIC-Based Measures of Phylogenetic Model Uncertainty
Reversible-jump Markov chain Monte Carlo (RJ-MCMC) is a technique for simultaneously evaluating multiple related (but not necessarily nested) statistical models that has recentl...
Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7
Bayesian inference of phylogeny using Markov chain Monte Carlo (MCMC) plays a central role in understanding evolutionary history from molecular sequence data. Visualizing and an...
MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space
Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With thi...
Model Averaging and Bayes Factor Calculation of Relaxed Molecular Clocks in Bayesian Phylogenetics
We describe a procedure for model averaging of relaxed molecular clock models in Bayesian phylogenetics. Our approach allows us to model the distribution of rates of substitutio...
Publication Info
- Year
- 1999
- Type
- article
- Volume
- 55
- Issue
- 1
- Pages
- 1-12
- Citations
- 543
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
543
OpenAlex
Cite This
Bob Mau,
Michael A. Newton,
Bret Larget
(1999).
Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods.
Biometrics
, 55
(1)
, 1-12.
https://doi.org/10.1111/j.0006-341x.1999.00001.x
Identifiers
- DOI
- 10.1111/j.0006-341x.1999.00001.x