Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods
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 m...
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Published in | Biometrics Vol. 55; no. 1; pp. 1 - 12 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.03.1999
International Biometric Society |
Subjects | |
Online Access | Get full text |
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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. |
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Bibliography: | istex:8940D9D7646FF649384914F584855D19BAAD529C ark:/67375/WNG-RNPT2Q8F-V ArticleID:BIOM1 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0006-341X 1541-0420 |
DOI: | 10.1111/j.0006-341X.1999.00001.x |