FH Eaton, Z Ghahramani
Choosing a Variable to Clamp: Approximate Inference Using Conditioned Belief Propagation
in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics
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.
