Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods

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Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods (1998)

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by Bob Mau , Michael A. Newton , Bret Larget

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BibTeX

@MISC{Mau98bayesianphylogenetic,
    author = {Bob Mau and Michael A. Newton and Bret Larget},
    title = {Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods},
    year = {1998}
}

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Abstract

This article is organized as follows: Section 2 opens with an introduction to the requisite terminology for tree representation. We present a general stochastic model for the evolution of discrete molecular data in Section 3. Section 4 articulates the Bayesian perspective and defines MetropolisHastings algorithms. In Section 5, we introduce a two-stage proposal distribution that randomly selects a canonical ordering of leaf labels, then acts on the superdiagonal of the corresponding cophenetic matrix. Section 6 describes restriction site data and posits a stochastic model for its evolution. MCMC is applied and key phylogenetic quantities are analyzed. We extend our method to nucleotide sequence data in Section 7. Section 8 describes runs made with computer-simulated DNA sequence data from our model and reconstruction. Finally, we summarize the advantages of our approach and discuss further extensions to more complex models. 2. Tree Terminology and Representation

Keyphrases

bayesian phylogenetic inference markov chain monte carlo method defines metropolishastings algorithm leaf label canonical ordering key phylogenetic quantity general stochastic model computer-simulated dna sequence data sequence data tree terminology describes restriction site data corresponding cophenetic matrix requisite terminology describes run complex model two-stage proposal distribution bayesian perspective stochastic model tree representation discrete molecular data

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