Bayesian computation via empirical likelihood

Mengersen, Kerrie L., Pudlo, Pierre, & Robert, Christian P. (2013) Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences, 110(4), pp. 1321-1326.

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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.

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16 citations in Scopus
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11 citations in Web of Science®

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ID Code: 59867
Item Type: Journal Article
Refereed: Yes
Keywords: autoregressive models, Bayesian statistics, likelihood-free methods, coalescent model
DOI: 10.1073/pnas.1208827110
ISSN: 1091-6490
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 13 Oct 2014 22:07
Last Modified: 15 Oct 2014 04:21

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