PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Choosing a Variable to Clamp: Approximate Inference Using Conditioned Belief Propagation
Frederik Eaton and Zoubin Ghahramani
In: AISTATS 2009, 16-18 April 2009, Clearwater, Florida, US.

Abstract

In this paper we propose an algorithm for approximate inference on graphical models based on belief propagation (BP). Our algorithm is an approximate version of Cutset Conditioning, in which a subset of variables is instantiated to make the rest of the graph singly connected. We relax the constraint of single-connectedness, and select variables one at a time for conditioning, running belief propagation after each selection. We consider the problem of determining the best variable to clamp at each level of recursion, and propose a fast heuristic which applies back-propagation to the BP updates. We demonstrate that the heuristic performs better than selecting variables at random, and give experimental results which show that it performs competitively with existing approximate inference algorithms.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5261
Deposited By:Frederik Eaton
Deposited On:24 March 2009