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Phylogenetic Inference for Binary Data on Dendograms Using Markov Chain Monte Carlo
Bob Mau and Michael A. Newton
Journal of Computational and Graphical Statistics
Vol. 6, No. 1 (Mar., 1997), pp. 122-131
Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of America
Stable URL: http://www.jstor.org/stable/1390728
Page Count: 10
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Using a stochastic model for the evolution of discrete characters among a group of organisms, we derive a Markov chain that simulates a Bayesian posterior distribution on the space of dendograms. A transformation of the tree into a canonical cophenetic matrix form, with distinct entries along its superdiagonal, suggests a simple proposal distribution for selecting candidate trees "close" to the current tree in the chain. We apply the consequent Metropolis algorithm to published restriction site data on nine species of plants. The Markov chain mixes well from random starting trees, generating reproducible estimates and confidence sets for the path of evolution.
Journal of Computational and Graphical Statistics © 1997 American Statistical Association