A1 Journal article (refereed)
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction (2020)
Vihola, M., & Franks, J. (2020). On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika, 107(2), 381-395. https://doi.org/10.1093/biomet/asz078
JYU authors or editors
Publication details
All authors or editors: Vihola, Matti; Franks, Jordan
Journal or series: Biometrika
ISSN: 0006-3444
eISSN: 1464-3510
Publication year: 2020
Volume: 107
Issue number: 2
Pages range: 381-395
Publisher: Oxford University Press
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1093/biomet/asz078
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/69182
Publication is parallel published: https://arxiv.org/abs/1902.00412
Abstract
Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure sufficient mixing and post-processing the output, leading to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators and propose an adaptive approximate Bayesian computation Markov chain Monte Carlo algorithm, which finds a balanced tolerance level automatically based on acceptance rate optimization. Our experiments show that post-processing-based estimators can perform better than direct Markov chain Monte Carlo targeting a fine tolerance, that our confidence intervals are reliable, and that our adaptive algorithm leads to reliable inference with little user specification.
Keywords: algorithms; Bayesian analysis; Markov chains; Monte Carlo methods
Free keywords: adaptive algorithm; approximate Bayesian computation; confidence interval; importance sampling; Markov chain Monte Carlo; tolerance choice
Contributing organizations
Related projects
- Exact approximate Monte Carlo methods for complex Bayesian inference
- Vihola, Matti
- Research Council of Finland
- Eksaktisti approksimoidut Monte Carlo -m
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- Research Council of Finland
- Exact approximate Monte Carlo methods for complex Bayesian inference (research costs)
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- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 2