Compressing Bayes Net CPTs with Persistent Leaky Causes

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Yang Xiang, Qian Jiang ;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:535-546, 2016.

Abstract

Non-Impeding Noisy-AND (NIN-AND) Trees (NATs) offer a highly expressive compressed casual model for significantly reducing space and inference time of Bayesian Nets (BNs). A causal model often includes a leaky cause for all causes not explicitly named. A leaky cause may be persistent or not. A conditional probability table (CPT) in a BN often behaves as if there is a persistent leaky cause (PLC). We discuss limitations for not modeling PLC explicitly during compression. We also reveal challenges if PLC is explicitly modeled. We extend an earlier solution that is limited to binary NAT models and is incomplete, to a solution that is applicable to multi-valued NAT models and is complete. We demonstrate the effectiveness of the solution experimentally for compressing general BN CPTs with PLCs.

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