Using Qualitative Relationships for Bounding Probability Distributions

Abstract

We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that are guaranteed to bound the distributions of interest in the desired direction. By evaluating incrementally improved approximate networks, our algorithm obtains monotonically tightening bounds that converge to exact distributions. For supermodular utility functions, the tightening bounds monotonically reduce the set of admissible decision alternatives as well.

Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)

References in corpus (5)

• Path Planning under Time-Dependent Uncertainty
• Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
• Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
• Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
• Propagation of 2-Monotone Lower Probabilities on an Undirected Graph

Cited by in corpus (1)

• Incremental Tradeoff Resolution in Qualitative Probabilistic Networks