Generalizing variable elimination in Bayesian networks
Fábio Gagliardi Cozman · 2000
. This paper describes a generalized version of the variable elimination algorithm for Bayesian networks. Variable elimination computes the marginal probability for some specified set of variables in a network. The algorithm consists of a single pass through a list of data structures called buckets. The generalization presented here adds a second pass to the algorithm and produces the marginal probability density for every variable in the buckets. The algorithm and the presentation focus on algebraic operations, striving for simplicity and easy of understanding. The algorithm has been implemented in the JavaBayes system, a freely distributed system for the construction and manipulation of Bayesian networks. 1