Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling

Robust Generalised Bayesian Inference for Intractable Likelihoods

Takuo Matsubara · Jeremias Knoblauch · Francois-Xavier Briol · Chris Oates


Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible misspecification 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 chain Monte Carlo.

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