期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 108, 期 37, 页码 15112-15117出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1102900108
关键词
Bayes factor; Bayesian model choice; likelihood-free methods; sufficient statistics; consistent tests
资金
- Agence Nationale de la Recherche
- National Science Foundation [1107070]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1107070] Funding Source: National Science Foundation
Approximate Bayesian computation (ABC) have become an essential tool for the analysis of complex stochastic models. Grelaud et al. [(2009) Bayesian Anal 3:427-442] advocated the use of ABC for model choice in the specific case of Gibbs random fields, relying on an intermodel sufficiency property to show that the approximation was legitimate. We implemented ABC model choice in a wide range of phylogenetic models in the Do It Yourself-ABC (DIY-ABC) software [Cornuet et al. (2008) Bioinformatics 24:2713-2719]. We now present arguments as to why the theoretical arguments for ABC model choice are missing, because the algorithm involves an unknown loss of information induced by the use of insufficient summary statistics. The approximation error of the posterior probabilities of the models under comparison may thus be unrelated with the computational effort spent in running an ABC algorithm. We then conclude that additional empirical verifications of the performances of the ABC procedure as those available in DIY-ABC are necessary to conduct model choice.
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