COMPUTATIONAL OPTIMIZATION FOR NORMAL FORM REALIZATION OF BAYESIAN MODEL GRAPHS

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) represent a very appealing paradigm for the realization of structures for probabilistic inference. Unfortunately, the computational and memory complexity of such networks remains high, especially if the network has to extend to large structures such as multi-layers and highly connected graphs. In this paper we focus on the details on probability propagation and learning that can reduce such complexity. More specifically we propose new algorithms and proceed to create a library that allows a significant reduction in costs with respect to direct use of the standard sums-products and Maximum Likelihood (ML) learning. Analysis and results are presented with reference to a Latent Variable Model (LVM).