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Everitt, Richard G. and Rowińska, Paulina A. (2021) Delayed acceptance ABC-SMC. Journal of Computational and Graphical Statistics, 30 (1). pp. 55-66. doi:10.1080/10618600.2020.1775617 ISSN 1061-8600.
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WRAP-Delayed-acceptance-ABC-SMC-Everitt-2020.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: https://doi.org/10.1080/10618600.2020.1775617
Abstract
Approximate Bayesian computation (ABC) is now an established technique for statistical inference used in cases where the likelihood function is computationally expensive or not available. It relies on the use of a model that is specified in the form of a simulator, and approximates the likelihood at a parameter value $\theta$ by simulating auxiliary data sets $x$ and evaluating the distance of $x$ from the true data $y$. However, ABC is not computationally feasible in cases where using the simulator for each $\theta$ is very expensive. This paper investigates this situation in cases where a cheap, but approximate, simulator is available. The approach is to employ delayed acceptance Markov chain Monte Carlo (MCMC) within an ABC sequential Monte Carlo (SMC) sampler in order to, in a~first stage of the kernel, use the cheap simulator to rule out parts of the parameter space that are not worth exploring, so that the ``true'' simulator is only run (in the second stage of the kernel) where there is a reasonable chance of accepting proposed values of $\theta$. We show that this approach can be used quite automatically, with few tuning parameters. Applications to stochastic differential equation models and latent doubly intractable distributions are presented.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Mathematical analysis, Monte Carlo method, Markov processes | |||||||||
Journal or Publication Title: | Journal of Computational and Graphical Statistics | |||||||||
Publisher: | American Statistical Association | |||||||||
ISSN: | 1061-8600 | |||||||||
Official Date: | 2021 | |||||||||
Dates: |
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Volume: | 30 | |||||||||
Number: | 1 | |||||||||
Page Range: | pp. 55-66 | |||||||||
DOI: | 10.1080/10618600.2020.1775617 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | “This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 02/06/2020, available online: http://www.tandfonline.com/10.1080/10618600.2020.1775617 | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 1 June 2020 | |||||||||
Date of first compliant Open Access: | 2 June 2021 | |||||||||
RIOXX Funder/Project Grant: |
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