A simple approach to Bayesian network computations

The general problem of computing poste rior probabilities in Bayesian networks is NP hard Cooper However e cient algorithms are often possible for particular applications by exploiting problem struc tures It is well understood that the key to the materialization of such a possibil ity is to make use of conditional indepen dence 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 fac torizing joint probabilities In comparison with the clique tree propagation approach our approach is very simple It allows the pruning of irrelevant variables it accommo dates changes to the knowledge base more easily it is easier to implement More importantly it can be adapted to utilize both intercausal independence and condi tional independence in one uniform frame work On the other hand clique tree prop agation is better in terms of facilitating pre computations