Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 268 - SBSS Student Paper Competition II
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322131
Title: Robust Generalised Bayesian Inference for Intractable Likelihoods
Author(s): Takuo Matsubara* and Jeremias Knoblauch and François-Xavier Briol and Chris Oates
Companies: Newcastle University and University College London and University College London and Newcastle University
Keywords: kernel methods; intractable likelihood; robust statistics; Stein’s method

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. On a theoretical level, we show consistency, asymptotic normality, and bias-robustness of the generalised posterior, highlighting how these properties are impacted by the choice of Stein discrepancy. Then, we provide numerical experiments on a range of intractable distributions, including applications to kernel-based exponential family models and non-Gaussian graphical models.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program