NAT model–based compression of Bayesian network CPTs over multivalued variables

Abstract

AbstractNonimpeding noisy‐AND tree (NAT) models offer a highly expressive approximate representation for significantly reducing the space of Bayesian networks (BNs). They also improve efficiency of BN inference significantly. To enable these advantages for general BNs, several technical advancements are made in this work to compress target BN conditional probability tables (CPTs) over multivalued variables into NAT models. We extend the semantics of NAT models beyond graded variables that causal independence models commonly adhered to and allow NAT modeling in nominal causal variables. We overcome the limitation of well‐defined pairwise causal interaction (PCI) bits and present a flexible PCI pattern extraction from target CPTs. We extend parameter estimation for binary NAT models to constrained gradient descent for compressing target CPTs over multivalued variables. We reveal challenges associated with persistent leaky causes and develop a novel framework for PCI pattern extraction when persistent leaky causes exist. The effectiveness of the CPT compression is validated experimentally.