PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nested sampling for Potts models
Iain Murray, David J.C. MacKay, Zoubin Ghahramani and John Skilling
In: NIPS 19, 5-8 Dec 2005, Vancouver, Canada.

Abstract

Nested sampling is a new Monte Carlo method by Skilling (2004) intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We demonstrate how this can be achieved for the Potts model, an undirected graphical model.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1167
Deposited By:Iain Murray
Deposited On:19 November 2005