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Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data

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Title: Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data
Author: Wang, Qian
Department: School of Computer Science
Program: Computer Science
Advisor: Xiang, Yang
Abstract: Bayesian Networks (BNs) are a widely utilized formalism for representing knowledge in intelligent agents on partially observable and stochastic application environments. When conditional probability tables are used in BNs to quantify strength of dependency between each variable and its parents, the space complexity is exponential on the number m of parents per variable. The time complexity of inference is also lower-bounded exponentially by m. The non-impeding noisy-AND Tree (NAT) model-based BNs can signi cantly improve both space and time complexity above, rendering both complexity measures linear on m, for a wide range of sparse BN structures. This research studies learning NAT model-based BNs from data by applying the Minimum Description Length principle and heuristic search. It advances BN structure learning with local models by focusing on inequality constraints. Practitioners can make tractable inferences using such BNs learned from data, especially when data admits high treewidth and low-density structures.
Date: 2020-02
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