Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization

Abstract

Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables.
Among inference methods for HBNs, we focus on dynamic discretization (DD)
that converts HBN to discrete BN for inference.
Complexity of BN inference is exponential on treewidth, which extends to DD for HBNs.
We presents a novel framework where HBN is transformed into NAT-modeled BN
(NAT: Non-impeding noisy-AND Tree) for tractable inference.
A case-study under the framework is presented on sum of continuous variables.
We report significant efficiency gain of approximate inference by NAT-modeled DD
over alternative methods.