Backward simulation in Bayesian networks

Authors:
Robert Fung;Brendan Del Favero
Affiliations:
Institute for Decision Systems Research, Los Altos, CA;Institute for Decision Systems Research, Los Altos, CA
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

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Abstract

Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evidence and works backward (i.e., contrary to the direction of the arcs). The technique's focus on the evidence leads to improved convergence in situations where the posterior beliefs are dominated by the evidence rather than by the prior probabilities. Since this class of situations is large, the technique may make practical the application of approximate inference in Bayesian belief networks to many real-world problems.