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Lewis P: Choosing among partition models in Bayesian phylogenetics
| Venue: | Mol Biol Evol |
| Citations: | 22 - 0 self |
BibTeX
@ARTICLE{Fan_lewisp:,
author = {Yu Fan and Rui Wu and Ming-hui Chen and Lynn Kuo and Paul O. Lewis},
title = {Lewis P: Choosing among partition models in Bayesian phylogenetics},
journal = {Mol Biol Evol},
year = {}
}
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Abstract
Bayesian phylogenetic analyses often depend on Bayes factors (BFs) to determine the optimal way to partition the data. The marginal likelihoods used to compute BFs, in turn, are most commonly estimated using the harmonic mean (HM) method, which has been shown to be inaccurate. We describe a new more accurate method for estimating the marginal likelihood of a model and compare it with the HM method on both simulated and empirical data. The new method generalizes our previously described stepping-stone (SS) approach by making use of a reference distribution parameterized using samples from the posterior distribution. This avoids one challenging aspect of the original SS method, namely the need to sample from distributions that are close (in the Kullback–Leibler sense) to the prior. We specifically address the choice of partition models and find that using the HM method can lead to a strong preference for an overpartitioned model. In contrast to the HM method and the original SS method, we show using simulated data that the generalized SS method is strikingly more precise (repeatable BF values of the same data and partition model) and yields BF values that are much more reasonable than those produced by the HM method. Comparisons of HM and generalized SS methods on an empirical data set demonstrate that the generalized SS method tends to choose simpler partition schemes that are more in line with expectation based on inferred patterns of molecular evolution. The generalized SS method shares with thermodynamic integration the need to sample from a series of distributions in addition to the posterior. Such dedicated path-based Markov chain Monte Carlo analyses appear to be a cost of estimating marginal likelihoods accurately.
Keyphrases
partition model hm method bayesian phylogenetics marginal likelihood original s method generalized s method new method empirical data set demonstrate bayes factor generalized s method share repeatable bf value reference distribution bayesian phylogenetic analysis yield bf value harmonic mean empirical data simpler partition scheme overpartitioned model accurate method inferred pattern s method posterior distribution thermodynamic integration molecular evolution optimal way kullback leibler sense strong preference
