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An Introduction to Approximate Bayesian Computation

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Statistics and Data Science (RSSDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1150))

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Abstract

Many modern statistical settings feature the analysis of data that may arise from unknown generating processes, or processes for which the generative models are computationally infeasible to interact with. Conventional estimation and inference solution methods in such settings may be unwieldy or impossible to implement. The approximate Bayesian computation (ABC) approach is a potent method in such scenarios, since it does not require the knowledge of the underlying generative model in order to perform inference. Furthermore, when combined with sufficiently regular discrepancy measurements such as the energy statistic, ABC can be shown to have desirable asymptotic properties. We provide a concise introduction to the general ABC framework. To demonstrate the capabilities and usefulness of the ABC approach, we present the analyses of a number of artificial examples as well as one of a real-data example pertaining to circular statistics data.

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Acknowledgements

The author is supported by Australian Research Council grants DE170101134 and DP180101192.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Latrobe University, Melbourne, Australia

    Hien D. Nguyen

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  1. Hien D. Nguyen
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Correspondence to Hien D. Nguyen .

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Editors and Affiliations

  1. La Trobe University, Bundoora, VIC, Australia

    Hien Nguyen

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Nguyen, H.D. (2019). An Introduction to Approximate Bayesian Computation. In: Nguyen, H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science, vol 1150. Springer, Singapore. https://doi.org/10.1007/978-981-15-1960-4_7

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  • DOI: https://doi.org/10.1007/978-981-15-1960-4_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1959-8

  • Online ISBN: 978-981-15-1960-4

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