BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:MCMC for doubly-intractable distributions - Iain Murray (Universit
 y of Toronto)
DTSTART:20080523T130000Z
DTEND:20080523T140000Z
UID:TALK11789@talks.cam.ac.uk
CONTACT:8047
DESCRIPTION:Markov chain Monte Carlo (MCMC) is a well-established framewor
 k for sampling from complex probability distributions. However\, standard 
 MCMC algorithms cannot sample from "doubly-intractable" distributions. \nD
 oubly-intractable distributions include the posterior over parameters of m
 any undirected graphical models and some point-process models. Every step 
 of a Markov chain seems to require the computation of an intractable norma
 lization term. \n\nThere 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 possib
 le and the implications for the Bayesian learning of undirected graphical 
 models. \n\nIf time allows I will share a recent insight by Ryan Adams\, w
 hich combined with MCMC algorithms for doubly-intractable distributions\, 
 allows Bayesian density estimation using Gaussian Processes. \n\nThis is w
 ork with David MacKay\, Zoubin Ghahramani and Ryan Adams. \n\n
LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0WB
END:VEVENT
END:VCALENDAR