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Price, Leah F., Drovandi, Christopher C., Lee, Anthony and Nott, David J. (2018) Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics, 27 (1). p. 1. doi:10.1080/10618600.2017.1302882
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WRAP-Bayesian-synthetic-likelihood-Lee-2017.pdf - Accepted Version - Requires a PDF viewer. Download (712Kb) | Preview |
Official URL: http://dx.doi.org/10.1080/10618600.2017.1302882
Abstract
Having the ability to work with complex models can be highly beneficial. However, complex models often have intractable likelihoods, so methods that involve evaluation of the likelihood function are infeasible. In these situations, the benefits of working with likelihood-free methods become apparent. Likelihood-free methods, such as parametric Bayesian indirect likelihood that uses the likelihood of an alternative parametric auxiliary model, have been explored throughout the literature as a viable alternative when the model of interest is complex. One of these methods is called the synthetic likelihood (SL), which uses a multivariate normal approximation of the distribution of a set of summary statistics. This paper explores the accuracy and computational efficiency of the Bayesian version of the synthetic likelihood (BSL) approach in comparison to a competitor known as approximate Bayesian computation (ABC) and its sensitivity to its tuning parameters and assumptions. We relate BSL to pseudo-marginal methods and propose to use an alternative SL that uses an unbiased
estimator of the SL, when the summary statistics have a multivariate normal distribution. Several applications of varying complexity are considered to illustrate the findings of this paper. Supplemental materials are available online.
| Item Type: | Journal Article | ||||||||
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| Divisions: | Faculty of Science > Statistics | ||||||||
| Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||||||
| Publisher: | American Statistical Association | ||||||||
| ISSN: | 1061-8600 | ||||||||
| Official Date: | 2018 | ||||||||
| Dates: |
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| Volume: | 27 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | p. 1 | ||||||||
| DOI: | 10.1080/10618600.2017.1302882 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access |
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