PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploiting within-clique factorizations in junction-tree algorithms
Julian McAuley and Tiberio Caetano
AISTATS pp. 525-532, 2010.

Abstract

We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a posteriori inference in graphical models can be improved. Our results apply whenever the potentials over maximal cliques of the triangulated graph are factored over subcliques. This is common in many real applications, as we illustrate with several examples. The new algorithms are easily implemented, and experiments show substantial speed-ups over the classical Junction-Tree Algorithm. This enlarges the class of models for which exact inference is efficient.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:7291
Deposited By:Julian McAuley
Deposited On:16 March 2011