The predictive nature of Bayesian inference
Chris Holmes
2025 Invited Talk
Abstract
We explore the predictive viewpoint of Bayesian inference that bypasses the conventional reliance on priors and likelihoods. By treating the joint predictive distribution of observables as the fundamental element, we gain fresh insights into the nature of Bayesian reasoning and derive principled generalizations – including new methods such as martingale posteriors. The predictive perspective improves our understanding of uncertainty quantification and can facilitate more adaptable, data-driven approaches to probabilistic modelling.
Speaker
Chris Holmes
Chris Holmes is Programme Director for Health and Medical Sciences at The Alan Turing Institute.
He is Professor of Biostatistics at the University of Oxford with a joint appointment between the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics and the Li Ka Shing Centre for Health Innovation and Discovery.
Before joining Oxford, Chris was based at Imperial College, London, and also worked in industry conducting research in scientific computing. He holds a Programme Leader’s award in Statistical Genomics from the Medical Research Council UK. In 2016, WIRED UK magazine named him one of the ‘Innovators of the year in AI’.
Chris has a broad interest in the theory, methods and applications of statistics and statistical modelling. He is particularly interested in pattern recognition and nonlinear, nonparametric statistical machine learning methods applied to the genomic sciences and genetic epidemiology.
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