Computing Bayes factors using thermodynamic integration.

Abstract:

:In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods. In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean estimation procedure. In the present article, we propose to employ another method, based on an analogy with statistical physics, called thermodynamic integration. We describe the method, propose an implementation, and show on two analytical examples that this numerical method yields reliable estimates. In contrast, the harmonic mean estimator leads to a strong overestimation of the marginal likelihood, which is all the more pronounced as the model is higher dimensional. As a result, the harmonic mean estimator systematically favors more parameter-rich models, an artefact that might explain some recent puzzling observations, based on harmonic mean estimates, suggesting that Bayes factors tend to overscore complex models. Finally, we apply our method to the comparison of several alternative models of amino-acid replacement. We confirm our previous observations, indicating that modeling pattern heterogeneity across sites tends to yield better models than standard empirical matrices.

journal_name

Syst Biol

journal_title

Systematic biology

authors

Lartillot N,Philippe H

doi

10.1080/10635150500433722

keywords:

subject

Has Abstract

pub_date

2006-04-01 00:00:00

pages

195-207

issue

2

eissn

1063-5157

issn

1076-836X

pii

G44K65W276345678

journal_volume

55

pub_type

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