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Acquisition of Causal Models for Local Distributions in Bayesian Networks

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dc.contributor.author Xiang, Yang
dc.contributor.author Truong, Minh
dc.date.accessioned 2015-06-19T19:13:22Z
dc.date.available 2015-06-19T19:13:22Z
dc.date.issued 2014
dc.identifier.citation Y. Xiang and M. Truong, Acquisition of Causal Models for Local Distributions in Bayesian Networks. IEEE Transactions on Cybernetics, Vol.44, No.9, 1591-1604, 2014. DOI: 10.1109/TCYB.2013.2290775 en_US
dc.identifier.issn 2168-2267
dc.identifier.uri http://hdl.handle.net/10214/8926
dc.description.abstract To specify a Bayesian network, a local distribution in the form of a conditional probability table, often of an effect conditioned on its n causes, needs to be acquired, one for each non-root node. Since the number of parameters to be assessed is generally exponential in n, improving the efficiency is an important concern in knowledge engineering. Non-impeding noisy-AND (NIN-AND) tree causal models reduce the number of parameters to being linear inn, while explicitly expressing both reinforcing and undermining interactions among causes. The key challenge in NIN-AND tree modeling is the acquisition of the NIN-AND tree structure. In this work, we formulate a concise structure representation and an expressive causal interaction function of NIN-AND trees. Building on these representations, we propose two structural acquisition methods, which are applicable to both elicitation-based and machine learning-based acquisitions. Their accuracy is demonstrated through experimental evaluations. en_US
dc.description.sponsorship NSERC, Canada en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Bayesian networks, Local distributions, Causal independence, Knowledge acquisition, Elicitation, Graphical models, Noisy-OR models, Causal modeling, NIN-AND trees en_US
dc.title Acquisition of Causal Models for Local Distributions in Bayesian Networks en_US
dc.type Article en_US
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