University of Cambridge > Talks.cam > The Archimedeans > Bayesian Inferences and the Laplace-Bernstein-von Mises Theorem

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Bayesian Inferences and the Laplace-Bernstein-von Mises Theorem

Download to your calendar using vCal

  • UserProfessor Richard Nickl
  • ClockFriday 20 October 2023, 18:00-19:00
  • HouseMR2 in the CMS.

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

Bayesian inference has been widely used in the statistical sciences and applied mathematics – systematically so since Laplace’s ‘Theorie analytique’ from 1812. It has seen a ‘dark age’ of subjectivist thinking in the 20th century due to computational infeasibility, but then emerged, with the advent of modern Markov chain Monte Carlo methods in the 1990s, as a popular paradigm for data driven inference under uncertainty. Nowadays Bayesian algorithms are among the most commonly used methods in statistics and machine learning. We will discuss some mathematical theorems that explain when one can trust such algorithms in the high-dimensional context of modern data science.

This talk is part of the The Archimedeans series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

links you can check FW26, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Physics of Living Matter Part III course (PLM)

Other talks

How Much Does Homology Really Know? Quantifying the unquantifiable? Assessing the importance of almsgiving in early modern France and Spain César Milstein Lecture - Title TBC Title TBC Online Causal Inference Seminar The organisational management of traumatic stress
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity