QUT Centre for Data Science: Post-Bayesian Statistics One-Day Workshop

AI is accelerating scientific model development, while the traditional statistical practices of carefully checking model assumptions are becoming sidelined.  At the same time, there is growing societal concern regarding the reliability of AI systems, prompting an urgent search for principled approaches to uncertainty quantification and robust decision-making.  This confluence of rapid deployment and foundational gaps presents a clear opportunity for the statistical sciences to play a central role.  It would be tempting to think that Bayesian inference offers a solution, combining a probabilistic model with prior knowledge to reason about uncertain parameters and to predict data that are yet to be observed.  Sadly this is optimistic, as Bayesian inference can fail spectacularly when either the model is misspecified or when meaningful prior information cannot be exploited.

Motivated by these fundamental challenges, there has been a recent surge of interest in post-Bayesian statistical methodology.  The “post-Bayesian” terminology refers broadly to methods which replace the traditional Bayesian updating of belief with alternative “updating” rules, with the aim of improving performance when modelling assumptions are violated.  This workshop is intended for both postgraduate students and established researchers who are familiar with Bayesian statistical methods and who would like to learn more about the emerging state-of-the-art in post-Bayesian methodology. The workshop is divided into three parts:

10:30–12:00   Part I. A tutorial on post-Bayesian statistical methods

12:00–13:00   Lunch

13:00–14:00   Part II. In detail: Predictively-oriented posteriors

14:00–14:30   Coffee break

14:30–15:30   Part III. Computation for post-Bayes

Professor Chris. J. Oates

Chris. J. Oates is a Professor in Statistics at Newcastle University in the UK.  His research spans Statistics, Machine Learning, and Computational Mathematics, and his contributions have been recognised with the award of the Research Prize of the Royal Statistical Society (RSS) in 2017, a Lloyd’s Register Foundation Fellowship (2019-2021), Alan Turing Institute Fellowships (2017-2026), the Leverhulme Prize for Mathematics and Statistics in 2023, and the William Guy Medal in Bronze from the RSS in 2024.