Lack of Confidence in Approximate Bayesian Computation Model Choice

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Lack of Confidence in Approximate Bayesian Computation Model Choice

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Title: Lack of Confidence in Approximate Bayesian Computation Model Choice
Author: Robert, Christian P.; Cornuet, Jean-Marie; Marin, Jean-Michel; Pillai, Natesh S

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Citation: Robert, Christian P., Jean-Marie Cornuet, Jean-Michel Marin, and Natesh S. Pillai. 2011. Lack of Confidence in Approximate Bayesian Computation Model Choice. Proceedings of the National Academy of Sciences 108, no. 37: 15112–15117.
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Abstract: 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.
Published Version: doi:10.1073/pnas.1102900108
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12724031
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