Distributed revision of composite beliefs
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Computing marginals for arbitrary subsets from marginal representation in Markov trees
Artificial Intelligence
Partial abductive inference in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Introduction to Bayesian Networks
Introduction to Bayesian Networks
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Abductive inference in bayesian networks: finding a partition of the explanation space
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Partial abductive inference in Bayesian belief networks has been usually expressed as an extension of total abductive inference (abduction over all the variables in the network). In this paper we study the transformation of the partial problem in a total one, analyzing and trying to improve the method previously appeared in the literature. We also outline an alternative approach, and compare both methods by means of experimentation. The experimental results reveal that the problem of partial abductive inference is difficult to solve by exact computation.