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Approximate Bayesian computation

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posted on 15.12.2021, 22:38 by Hien NguyenHien Nguyen, Fazel AlmasiFazel Almasi
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.

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Intellectual Climate Fund

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  • School of Life Sciences

Publication Date

16/12/2021

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The Author reserves all moral rights over the deposited item and must be credited if any re-use occurs.

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