Active Tuples-based Scheme for Bounding Posterior Beliefs
This work addresses the challenge of efficient and accurate inference in Bayesian networks for probabilistic reasoning applications, but it is incremental as it builds on existing bounding schemes.
The paper tackles the problem of computing bounds on posterior marginals in Bayesian networks by introducing a scheme that enhances existing bounding methods using cutset conditioning, with accuracy improving as more cutset tuples are used, though at increased computation time. It demonstrates empirical value using a bound propagation variant as a plug-in.
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.