Statistics seminar

​Emilia Pompe, University of Oxford: "Robust inference using Posterior Bootstrap"

​Abstract: Bayesian inference is known to provide misleading uncertainty estimation when the considered model is misspecified. This talk will explore various alternatives to standard Bayesian inference under model misspecification, based on extensions of the Weighted Likelihood Bootstrap (Newton & Raftery, 1994).

In the first part, we will talk about Posterior Bootstrap, which is an extension of Weighted Likelihood Bootstrap allowing the user to properly incorporate the prior. We will see how Edgeworth expansions can be used to understand the impact of the prior and guide the choice of hyperparameters.

Next we will talk about Bayesian models built of multiple components having shared parameters. Misspecification of any part of the model might propagate to all other parts and lead to unsatisfactory results. Cut distributions have been proposed as a remedy, where the information is prevented from flowing along certain directions. We will show that asymptotically cut distributions don't have the correct frequentist coverage for the associate credible regions. We will then discuss our new alternative methodology, based on the Posterior Bootstrap, which delivers credible regions with the nominal frequentist asymptotic coverage.

The talk is based on the following papers: (joint work with Pierre Jacob)
​Due to recent restrictions the seminars will be on zoom only. Members of the department will receive the link and password via mail. Others interested are welcome to get the link by contacting

Organiser: Umberto Picchini ( Please contact me if you wish to be informed of future seminars.

Category Seminar
Location: Online
Starts: 18 January, 2022, 14:15
Ends: 18 January, 2022, 15:15

Page manager Published: Thu 13 Jan 2022.