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• W1983593488 abstract "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." @default.
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• W1983593488 date "2013-01-07" @default.
• W1983593488 modified "2023-10-16" @default.
• W1983593488 title "Bayesian computation via empirical likelihood" @default.
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• W1983593488 doi "https://doi.org/10.1073/pnas.1208827110" @default.
• W1983593488 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3557074" @default.
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