Empirical and hierarchical Bayesian estimation of ancestral states Journal Article

Author(s): Huelsenbeck, John P; Bollback, Jonathan P
Article Title: Empirical and hierarchical Bayesian estimation of ancestral states
Abstract: Several methods have been proposed to infer the states at the ancestral nodes on a phylogeny. These methods assume a specific tree and set of branch lengths when estimating the ancestral character state. Inferences of the ancestral states, then, are conditioned on the tree and branch lengths being true. We develop a hierarchical Bayes method for inferring the ancestral states on a tree. The method integrates over uncertainty in the tree, branch lengths, and substitution model parameters by using Markov chain Monte Carlo. We compare the hierarchical Bayes inferences of ancestral states with inferences of ancestral states made under the assumption that a specific tree is correct. We find that the methods are correlated, but that accommodating uncertainty in parameters of the phylogenetic model can make inferences of ancestral states even more uncertain than they would be in an empirical Bayes analysis.
Keywords: Animals; Humans; Models, Genetic; Likelihood Functions; Phylogeny; Bayes Theorem; Monte Carlo Method; DNA/genetics; Markov Chains; Dna; Ancestral state reconstruction; Bayesian estimation; empirical Bayes; hierarchical Bayes
Journal Title: Systematic Biology
Volume: 50
Issue 3
ISSN: 1063-5157
Publisher: Oxford University Press  
Date Published: 2001-05-01
Start Page: 351
End Page: 366
DOI: 10.1080/10635150119871
Open access: no