University of Cambridge > > Mordell Lectures > Bayesian inference in infinite dimensions

Bayesian inference in infinite dimensions

Add to your list(s) Download to your calendar using vCal

  • UserAad van der Vaart (Delft)
  • ClockThursday 04 May 2023, 17:00-18:00
  • HouseMR2, CMS.

If you have a question about this talk, please contact HoD Secretary, DPMMS.

The Bayesian statistical method consists of updating a prior probability distribution over the unknown parameters of a stochastic system into a posterior probability distribution after seeing the system’s output. It is perhaps the oldest statistical paradigm, going back to the 18th century, in abstract terms as straightforward and elegant as can be, and with the promise of not only giving a best guess of the system parameters, but also a quantification of remaining uncertainty. Only in the last two decades has the method been applied to infinite-dimensional parameters, most recently to inverse problems defined e.g. by PDEs or in machine learning. We discuss some of the mathematical issues, with a main focus on the question whether the method works and when, and how we can define “works”. We review some classical success stories and recent findings and open questions, borrowing from our own work and that of others.

The talk will be followed by a wine reception in the Central Core CMS

This talk is part of the Mordell Lectures series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity