A simple approach to Bayesian network computations (original) (raw)

The general problem of computing posterior probabilities in Bayesian networks is NP-hard (Cooper 1990). However e cient algorithms are often possible for particular applications by exploiting problem structures. It is well understood that the key to the materialization of such a possibility i s t o m a k e use of conditional independence and work with factorizations of joint probabilities rather than joint probabilities themselves. Di erent exact approaches can be characterized in terms of their choices of factorizations. We propose a new approach which adopts a straightforward way for factorizing joint probabilities. In comparison with the clique tree propagation approach, our approach i s v ery simple. It allows the pruning of irrelevant v ariables, it accommodates changes to the knowledge base more easily. it is easier to implement. More importantly, it can be adapted to utilize both intercausal independence and conditional independence in one uniform framework. On the other hand, clique tree propagation is better in terms of facilitating precomputations.