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Bayesian computation via empirical likelihood
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Mengersen, K. L., Pudlo, P. and Robert, Christian P. (2013) Bayesian computation via empirical likelihood. Proceedings of the National Academy of Sciences of the United States of America, Volume 110 (Number 4). pp. 1321-1326. doi:10.1073/pnas.1208827110 ISSN 0027-8424.
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Official URL: http://dx.doi.org/10.1073/pnas.1208827110
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Proceedings of the National Academy of Sciences of the United States of America | ||||||||
Publisher: | National Academy of Sciences | ||||||||
ISSN: | 0027-8424 | ||||||||
Official Date: | 22 January 2013 | ||||||||
Dates: |
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Volume: | Volume 110 | ||||||||
Number: | Number 4 | ||||||||
Page Range: | pp. 1321-1326 | ||||||||
DOI: | 10.1073/pnas.1208827110 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
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