Reformulating Inference Problems through Selective Conditioning

Reference: Dagum, P. & Horvitz, E. J. Reformulating Inference Problems through Selective Conditioning. Stanford, CA, 1992.

Abstract: We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algortihm. We employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the constribution the nodes make to the tractability of stochastic simulation. We review previous work on BNRAS algorithms-randomized approximation algorithms for probabilistic inference. We show how selective conditioning can be employed to reformulate a single BNRAS problem into multiple tractable BNRAS simulation problems. We discuss how we can use another simulation algorithm-logic sampling-to solve a component of the inference problem that provides a means for knitting the solutions of individual subproblems into a final result. Finally, we analyze tradeoffs among the computational subtasks associated with the selective conditioning approach to reformulation.

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