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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.

Author Information

Takuo Matsubara (Newcastle University, UK)
Jeremias Knoblauch (Warwick University)
Francois-Xavier Briol (Alan Turing Institute)
Chris Oates (Newcastle University)

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