Efficient indexing methods for recursive decompositions of Bayesian networks

We consider efficient indexing methods for conditioning graphs, which are a form of recursive decomposition for Bayesian networks. We compare two well-known methods for indexing, a top-down method and a bottom-up method, and discuss the redundancy that each of these suffer from. We present a new method for indexing that combines the advantages of each model in order to reduce this redundancy. We also introduce the concept of an update manager, which is a node in the conditioning graph that controls when other nodes update their current index. Empirical evaluations over a suite of standard test networks show a considerable reduction both in the amount of indexing computation that takes place, and the overall runtime required by the query algorithm.

► A new model for indexing the probability tables in a conditioning graph is proposed.
► This new model is a hybrid of two existing indexing approaches.
► Several optimizations to the new technique are also demonstrated.
► Tests on benchmark networks show improvements to existing indexing methods.