Approximate Bayesian Computation Using Indirect Inference
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Summary: We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.
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The key strength of ABC approaches is that they can be applied to problems with intractable likelihoods.
“The key strength of ABC approaches is that they can be applied to problems with intractable likelihoods35,36 (for example, complex stochastic models).”
From A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation (2014) · cites this paper as [36]
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- Authors
- Drovandi, C. C., Pettitt, A. N., & Faddy, M. J.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- Drovandi, Christopher C.
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-01-13
- Volume
- 60
- Issue
- 3
- Pages
- 317-337
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W2121052473
- Cited by
- 100
- References
- 26
- Citations by year
- 2025: 6 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 10 · 2019: 5 · 2018: 11 · 2017: 6 · 2016: 3 · 2015: 14 · 2014: 7 · 2013: 4 · 2012: 6
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.qut.edu.au
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series C (Applied Statistics) (publishedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion)
- Funders
- Australian Research Council
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- approximate Bayesian computation, indirect inference, summary statistics, auxiliary model, sequential Monte Carlo, intractable likelihood, beta-binomial distribution, parameter identifiability
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