Optimizing Bayesian belief networks: A case study of information retrieval systems

Bayesian Belief networks have been used widely to solve many decision problem that involve uncertainty. One major advantage of this approach compared with other reasoning tools is its semantic richness in describing the decision process. Some inference algorithms for carrying out the reasoning process exist, such as those of Pearl and of Lauritzen, however, these algorithms are known to be computationally expensive. Hence, they require optimization to make them practical. This paper proposes two optimization techniques for Bayesian Belief networks. These optimization techniques were investigated for Information Retrieval applications, but can also be applied to different applications outside the information retrieval area.