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Parsimony, Phylogeny, and Genomics$
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Victor A. Albert

Print publication date: 2006

Print ISBN-13: 9780199297306

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780199297306.001.0001

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Parsimony and Bayesian phylogenetics

Parsimony and Bayesian phylogenetics

Chapter:
(p.148) Chapter 8 Parsimony and Bayesian phylogenetics
Source:
Parsimony, Phylogeny, and Genomics
Author(s):

Pablo A. Goloboff

Diego Pol

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199297306.003.0008

The intent of a statistically-based phylogenetic method is to estimate tree topologies and values of possibly relevant parameters, as well as the uncertainty inherent in those estimations. A method that could do that with reasonable accuracy would be attractive indeed. It is often claimed that it is advantageous for a method to be based on a specific evolutionary model, because that allows incorporating into the analysis the ‘knowledge’ of the real world embodied in the model. Bayesian methods have become very prominent among model-based methods, in part because of computational advantages, and in part because they estimate the probability that a given hypothesis is true, given the observations and model assumptions. Through simulation studies, this chapter finds that even if there is the potential for Bayesian estimations of monophyly to provide correct topological estimations for infinite numbers of characters, the resulting claims to measure degrees of support for conclusions — in a statistical sense — are unfounded. Additionally, for large numbers of terminals, it is argued to be extremely unlikely that a search via Markov Monte Carlo techniques would ever pass through the optimal tree(s), let alone pass through the optimal tree(s) enough times to estimate their posterior probability with any accuracy.

Keywords:   Bayesian methods, evolutionary model, likelihood, posterior probability, support, Markov chain, Monte Carlo, MCMC

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