Active Tuples-based Scheme for Bounding Posterior Beliefs
Venue
JAIR, vol. 39 (2010), pp. 335-371
Publication Year
2010
Authors
Bozhena Bidyuk, Rina Dechte, Emma Rollon
BibTeX
Abstract
The paper presents a scheme for computing lower and upper bounds on the posterior
marginals in Bayesian networks with discrete variables. Its power lies in its
ability to use any available scheme that bounds the probability of evidence or
posterior marginals and enhance its performance in an anytime manner. The scheme
uses the cutset conditioning principle to tighten existing bounding schemes and to
facilitate anytime behavior, utilizing a fixed number of cutset tuples. The
accuracy of the bounds improves as the number of used cutset tuples increases and
so does the computation time. We demonstrate empirically the value of our scheme
for bounding posterior marginals and probability of evidence using a variant of the
bound propagation algorithm as a plug-in scheme.
