Robust Generalised Bayesian Inference for Intractable Likelihoods. (arXiv:2104.07359v3 [stat.ME] UPDATED)

Generalised Bayesian inference updates prior beliefs using a loss function,
rather than a likelihood, and can therefore be used to confer robustness
against possible mis-specification of the likelihood. Here we consider
generalised Bayesian inference with a Stein discrepancy as a loss function,
motivated by applications in which the likelihood contains an intractable
normalisation constant. In this context, the Stein discrepancy circumvents
evaluation of the normalisation constant and produces generalised posteriors
that are either closed form or accessible using standard Markov …

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