Efficient acquisition rules for model-based approximate Bayesian computation

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Edinburgh

Abstract

Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution. Our paper addresses this gap in the literature. We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty. We then propose to select the next evaluation location to minimise the expected loss. Experiments show that the proposed method often produces the most accurate approximations as compared to common BO strategies.

Details

Original languageEnglish
Pages (from-to)595-622
Number of pages28
JournalBayesian Analysis
Volume14
Issue number2
Publication statusPublished - Jun 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Approximate Bayesian computation, Bayesian optimisation, Gaussian processes, Intractable likelihood, Sequential experiment design, MONTE-CARLO, STATISTICAL-INFERENCE, REDUCTION, sequential experiment design, approximate Bayesian computation, intractable likelihood

ID: 33938263