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	<pub:InProceedings rdf:about="http://ebiquity.umbc.edu/paper/html/id/236/Modifying-Bayesian-Networks-by-Probability-Constraints">
		<rdfs:label><![CDATA[Modifying Bayesian Networks by Probability Constraints]]></rdfs:label>
		<pub:title><![CDATA[Modifying Bayesian Networks by Probability Constraints]]></pub:title>
		<pub:publishedOn rdf:datatype="&xsd;dateTime">2005-07-26T00:00:00-05:00</pub:publishedOn>
		<pub:abstract><![CDATA[This paper deals with the following problem:
modify a Bayesian network to satisfy a given set
of probability constraints by only changeing its
conditional probability tables while keeping the probability
distribution of the resulting network  as
close as possible to that of the original.
We solve this problem by extending
IPFP (iterative proportional fitting procedure) to
probability distributions represented by Bayesian
networks. The resulting algorithm, E-IPFP is further
developed to D-IPFP, which reduces the
computational cost by decomposing a global EIPFP
into a set of smaller local E-IPFP problems.  We provide a
limited analysis, including the convergence
proofs of the two algorithms. Computer
experiments were conducted to validate the algorithms.
The results are consistent with the theoretical
analysis.]]></pub:abstract>
		<pub:counter>2981</pub:counter>
		<pub:googleKey>z-9klSFL6EgJ</pub:googleKey>
		<pub:googleCitations>18</pub:googleCitations>
		<pub:tag><![CDATA[bayesian reasoning]]></pub:tag>
		<pub:tag><![CDATA[learning]]></pub:tag>
		<pub:tag><![CDATA[uncertainty]]></pub:tag>
		<pub:booktitle><![CDATA[Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence]]></pub:booktitle>
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					<person:Person rdf:about="http://ebiquity.umbc.edu/person/html/Yun/Peng"><person:name><![CDATA[Yun Peng]]></person:name><rdfs:label><![CDATA[Yun Peng]]></rdfs:label></person:Person>
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