Efficient Bayesian synthetic likelihood with whitening transformations

Jacob W. Priddle, Scott A. Sisson, David T. Frazier, Ian Turner, Christopher Drovandi

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computationally demanding. Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution—typically Gaussian—and then performs statistical inference using standard likelihood-based techniques. However, as the number of summary statistics grows, the number of model simulations required to accurately estimate the covariance matrix for this likelihood rapidly increases. This poses a significant challenge for the application of BSL, especially in cases where model simulation is expensive. In this article, we propose whitening BSL (wBSL)—an efficient BSL method that uses approximate whitening transformations to decorrelate the summary statistics at each algorithm iteration. We show empirically that this can reduce the number of model simulations required to implement BSL by more than an order of magnitude, without much loss of accuracy. We explore a range of whitening procedures and demonstrate the performance of wBSL on a range of simulated and real modeling scenarios from ecology and biology. Supplementary materials for this article are available online.

Original languageEnglish
Number of pages14
JournalJournal of Computational and Graphical Statistics
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Approximate Bayesian computation
  • Covariance matrix estimation
  • likelihood-free inference
  • Markov chain Monte Carlo
  • Shrinkage estimation

Cite this