@inproceedings{dagum1992reformulating,
author = {Dagum, Paul and Horvitz, Eric},
title = {Reformulating Inference Problems Through Selective Conditioning},
booktitle = {UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence},
year = {1992},
month = {June},
abstract = {We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/reformulating-inference-problems-selective-conditioning/},
pages = {49-54},
isbn = {1-55860-258-5},
edition = {UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence},
}