2022
DOI: 10.48550/arxiv.2203.09782
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Modularized Bayesian analyses and cutting feedback in likelihood-free inference

Abstract: There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference method is "cutting feedback" which can be used when the model consists of a number of coupled modules, with only some of the modules being misspecified. Cutting feedback methods represent the full posterior distribution in terms of conditional and sequential components, and then modify some terms in such a representation based on the mod… Show more

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