Active Information Fusion
Factor Tree Inference Algorithm for Bayesian Networks and its Applications
In a Bayesian network, a probabilistic inference is the procedure of computing
the posterior probability of query variables given a collection of evidences.
We propose an algorithm that efficiently carries out the inferences whose
query variables and evidence variables are restricted to a subset of the set
of the variables in a BN. The algorithm successfully combines the advantages
of two popular inference algorithms – variable elimination and clique tree
propagation. We empirically demonstrate its computational efficiency in an
affective computing domain.