Approximation of General CPTs with NIN-AND Tree Models

dc.contributor.advisorXiang, Yang
dc.contributor.authorJiang, Qian
dc.date.accessioned2017-01-05T18:45:19Z
dc.date.available2017-01-05T18:45:19Z
dc.date.copyright2016-11
dc.date.created2016-12-02
dc.date.issued2017-01-05
dc.degree.departmentSchool of Computer Scienceen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeComputer Scienceen_US
dc.description.abstractA Bayesian network (BN) is a probabilistic graphical model representing causal dependence and conditional independence among domain variables. The strength of dependence is quantified by conditional probability tables (CPTs). The space complexity of BNs is exponential on the number of parents per variable, which limits the effectiveness of knowledge acquisition and inference. Non-impeding noisy-AND tree (NAT) models give a highly expressive approximate representation of BNs. Approximating CPTs in BNs by NAT models can significantly reduce the space complexity and speed up BN inference. In this thesis, we generalize the CPT approximation by NAT models. Leaky causes, representing all causes that are not explicitly modeled, exist in CPTs from real world BNs. We approximate CPTs with persistent leaky causes (PLCs) by explicitly modeling such leaky causes. Experimental results show that approximating general CPTs by NAT models is reasonably accurate. Explicitly modeling PLCs further improves the accuracy in CPTs with PLCs.en_US
dc.identifier.urihttp://hdl.handle.net/10214/10164
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectBayesian Networken_US
dc.subjectCPTen_US
dc.subjectNIN-AND Tree Modelen_US
dc.titleApproximation of General CPTs with NIN-AND Tree Modelsen_US
dc.typeThesisen_US

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