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Learning tractable NAT-modeled Bayesian networks

Learning tractable NAT-modeled Bayesian networks Bayesian networks (BNs) encode conditional independence to avoid combinatorial explosion on the number of variables, but are subject to exponential growth of space and inference time on the number of causes per effect variable. Among space-efficient local models, we focus on the Non-Impeding Noisy-AND Tree (NIN-AND Tree or NAT) models, due to their multiple merits, and on NAT-modeled BNs, where each multi-parent variable family may be encoded as a NAT-model. Although BN inference is generally exponential on treewidth, the inference is tractable with NAT-modeled BNs of high treewidth and low density. In this work, we present the first study to learn NAT-modeled BNs from data. We apply the MDL principle to learning NAT-modeled BNs by developing a corresponding scoring function, and we couple it with heuristic structure search. We show that when data satisfy NAT causal independence, high treewidth, and low density structure, learning underlying NAT modeled BNs is feasible. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Learning tractable NAT-modeled Bayesian networks

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References (16)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-021-09748-0
Publisher site
See Article on Publisher Site

Abstract

Bayesian networks (BNs) encode conditional independence to avoid combinatorial explosion on the number of variables, but are subject to exponential growth of space and inference time on the number of causes per effect variable. Among space-efficient local models, we focus on the Non-Impeding Noisy-AND Tree (NIN-AND Tree or NAT) models, due to their multiple merits, and on NAT-modeled BNs, where each multi-parent variable family may be encoded as a NAT-model. Although BN inference is generally exponential on treewidth, the inference is tractable with NAT-modeled BNs of high treewidth and low density. In this work, we present the first study to learn NAT-modeled BNs from data. We apply the MDL principle to learning NAT-modeled BNs by developing a corresponding scoring function, and we couple it with heuristic structure search. We show that when data satisfy NAT causal independence, high treewidth, and low density structure, learning underlying NAT modeled BNs is feasible.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Nov 1, 2021

Keywords: Bayesian networks; Causal independence models; Probabilistic inference; Local structures; Machine Learning; 68T05; 68T37

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