BSU Seminar: "Predictive resampling for scalable Bayes"

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• Edwin Fong, Department of Statistics and Actuarial Science, University of Hong Kong
• Tuesday 04 November 2025, 10:00-11:00
• Online Seminar.

If you have a question about this talk, please contact Alison Quenault.

This will be a free online seminar. To register to attend, please click here: https://cam-ac-uk.zoom.us/meeting/register/gELlOqxOT3imWtS8MRVYRw

The martingale posterior is a framework for Bayesian inference in which posterior uncertainty is generated through predictive imputation. A key advantage of this approach is that the Bayesian model can be directly specified using a sequence of predictive distributions. This eliminates the need for explicit likelihood and prior specifications, thereby avoiding the computational demands of MCMC . Instead, posterior sampling for martingale posteriors relies on predictive resampling—a parallelizable, bootstrap-like procedure that is highly efficient. This talk will highlight recent computational advances in martingale posteriors, enabling scalable posterior inference for both nonparametric and parametric models.

This talk is part of the MRC Biostatistics Unit Seminars series.

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