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Mixing ICI and CSI Models for More Efficient Probabilistic Inference

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Title: Mixing ICI and CSI Models for More Efficient Probabilistic Inference
Author: Roher, Michael
Department: School of Computer Science
Program: Computer Science
Advisor: Xiang, Yang
Abstract: Bayesian Networks (BNs) concisely represent probabilistic knowledge of uncertain environments by encoding causal dependencies and exploiting conditional independencies between variables. The strength of each variable's dependence on its parents is quantified by a conditional probability table (CPT). However, these CPTs suffer from an exponential growth on the number of parents. To address the exponential growth, various local models have been introduced for representational savings and further inference efficiency. Some exploit context-specific independence (CSI), which concisely encode duplicated probabilities. Others exploit independence of causal influence (ICI), which encode causal relationships between variables. Existing techniques apply only ICI or only CSI in a BN, such that exploiting one model sacrifice savings yielded by the other. We develop an exact inference framework for BNs modelled with both: We apply Non-Impeding Noisy-AND Trees for ICI, and CPT-trees for CSI. The experimental evaluation demonstrates a significant inference efficiency gain beyond what is attainable by exploiting only one type of model.
Date: 2020
Rights: Attribution 4.0 International

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