Thermodynamics Research Today is a free monthly online journal that collates and summarizes the latest research about Thermodynamics, including details on enthalpy, entropy, energy transitions.

Computing Bayes factors using thermodynamic integration.

Lartillot N, Philippe H

Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier UMR 5506, CNRS-Université, de Montpellier 2, 161, rue Ada, 34392 Montpellier Cedex 5, France. nicolas.lartillot@lirmm.fr

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.

Published 8 March 2006 in Syst Biol, 55(2): 195-207.
Full-text of this article is available online (may require subscription).

© 2005-2008 Thermodynamics Research Today. All Rights Reserved.