An empirical study of Bayesian network inference with simple propagation

Highlights

• Simple Propagation (SP) is a new algorithm for Bayesian network inference.

• SP exploits probabilistic conditional independencies without explicitly testing for them.

• SP tends to be faster than Lazy Propagation in optimal join trees.

Abstract

We propose Simple Propagation (SP) as a new join tree propagation algorithm for exact inference in discrete Bayesian networks. We establish the correctness of SP. The striking feature of SP is that its message construction exploits the factorization of potentials at a sending node, but without the overhead of building and examining graphs as done in Lazy Propagation (LP). Experimental results on optimal (or close to optimal) join trees built from numerous benchmark Bayesian networks show that SP is often faster than LP.