Bayesian computation via empirical likelihood.
Robert, Christian P.; Pudlo, Pierre; Mengersen, Kerrie (2013), Bayesian computation via empirical likelihood., Proceedings of the National Academy of Sciences of the United States of America, 110, 4, p. 1321-1326. http://dx.doi.org/10.1073/pnas.1208827110
TypeArticle accepté pour publication ou publié
External document linkhttp://fr.arxiv.org/abs/1205.5658
Journal nameProceedings of the National Academy of Sciences of the United States of America
MetadataShow full item record
Abstract (EN)Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.
Subjects / Keywordscoalescent model; likelihood-free methods; Bayesian statistics; autoregressive models
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