University of Cambridge > Talks.cam > Statistics > MCMC for doubly-intractable distributions

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

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

ℹī¸ Information

MCMC for doubly-intractable distributions

Download to your calendar using vCal

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

Markov chain Monte Carlo (MCMC) is a well-established framework for sampling from complex probability distributions. However, standard MCMC algorithms cannot sample from “doubly-intractable” distributions. Doubly-intractable distributions include the posterior over parameters of many undirected graphical models and some point-process models. Every step of a Markov chain seems to require the computation of an intractable normalization term.

There are a growing number of valid MCMC algorithms for doubly-intractable distributions. They all involve daunting computations, but at least give insight into the problem. I will review what is possible and the implications for the Bayesian learning of undirected graphical models.

If time allows I will share a recent insight by Ryan Adams, which combined with MCMC algorithms for doubly-intractable distributions, allows Bayesian density estimation using Gaussian Processes.

This is work with David MacKay, Zoubin Ghahramani and Ryan Adams.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Bullard Labs tearoom, Department of Earth Sciences, Madingley Rise LR12, Engineering, Department of Seminar Room MR4, Centre for Mathematical Sciences, Wilberforce Road, Cambridge

Other talks

TBC Bigger Picture Talk: Developing future marine engines running on ammonia and other green fuels The uplift and erosion history of the Gamburtsev Subglacial Mountains and their role in the formation of the East Antarctic Ice Sheet Talk TBC TBC How Much Does Homology Really Know?
 

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