A1105
Title: Calibrated generalized Bayesian inference
Authors: Robert Kohn - University of New South Wales (Australia) [presenting]
David Frazier - Monash University (Australia)
Christopher Drovandi - Queensland University of Technology (Australia)
Abstract: The aim is to provide a simple and general solution to the fundamental open problem of inaccurate uncertainty quantification of Bayesian inference in misspecified or approximate models and of generalized Bayesian posteriors more generally. While existing solutions are based on explicit Gaussian posterior approximations or computationally onerous post-processing procedures, correct uncertainty quantification is demonstrated as achievable by substituting the usual posterior with an alternative posterior that conveys the same information. This solution applies to both likelihood-based and loss-based posteriors, and the reliable uncertainty quantification of this approach is formally demonstrated. The new approach is demonstrated through a range of examples, including generalized linear models and doubly intractable models.
