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An automatic adaptive method to combine summary statistics in approximate Bayesian computation
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Harrison, Jonathan and Baker, Ruth E. (2020) An automatic adaptive method to combine summary statistics in approximate Bayesian computation. PLoS One, 15 (8). e0236954. doi:10.1371/journal.pone.0236954 ISSN 1932-6203.
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WRAP-An-automatic-adaptive-method-combine-statistics-Bayesian-computation-Harrison-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1029Kb) | Preview |
Official URL: https://doi.org/10.1371/journal.pone.0236954
Abstract
To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > QA Mathematics | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Mathematical analysis | |||||||||||||||
| Journal or Publication Title: | PLoS One | |||||||||||||||
| Publisher: | Public Library of Science | |||||||||||||||
| ISSN: | 1932-6203 | |||||||||||||||
| Official Date: | 6 August 2020 | |||||||||||||||
| Dates: |
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| Volume: | 15 | |||||||||||||||
| Number: | 8 | |||||||||||||||
| Article Number: | e0236954 | |||||||||||||||
| DOI: | 10.1371/journal.pone.0236954 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 13 August 2020 | |||||||||||||||
| Date of first compliant Open Access: | 18 August 2020 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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