<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-27T16:20:11Z</responseDate><request verb="GetRecord" identifier="oai:docta.ucm.es:20.500.14352/33619" metadataPrefix="mods">https://docta.ucm.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:docta.ucm.es:20.500.14352/33619</identifier><datestamp>2024-07-15T12:57:38Z</datestamp><setSpec>com_20.500.14352_14</setSpec><setSpec>col_20.500.14352_15</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
   <mods:name>
      <mods:namePart>Gómez Villegas, Miguel Ángel</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Main Yaque, Paloma</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Viviani, Paola</mods:namePart>
   </mods:name>
   <mods:extension>
      <mods:dateAvailable encoding="iso8601">2023-06-19T13:25:07Z</mods:dateAvailable>
   </mods:extension>
   <mods:extension>
      <mods:dateAccessioned encoding="iso8601">2023-06-19T13:25:07Z</mods:dateAccessioned>
   </mods:extension>
   <mods:originInfo>
      <mods:dateIssued encoding="iso8601">2014-08-10</mods:dateIssued>
   </mods:originInfo>
   <mods:identifier type="citation">Gómez Villegas, M. Á., Main Yaque, P. &amp; Viviani, P. «Sensitivity to Evidence in Gaussian Bayesian Networks Using Mutual Information». Information Sciences, vol. 275, agosto de 2014, pp. 115-26. DOI.org (Crossref), https://doi.org/10.1016/j.ins.2014.02.025.</mods:identifier>
   <mods:identifier type="issn">0020-0255</mods:identifier>
   <mods:identifier type="doi">10.1016/j.ins.2014.02.025</mods:identifier>
   <mods:identifier type="uri">https://hdl.handle.net/20.500.14352/33619</mods:identifier>
   <mods:identifier type="officialurl">https//doi.org/10.1016/j.ins.2014.02.025</mods:identifier>
   <mods:identifier type="relatedurl">http://www.sciencedirect.com/science/article/pii/S0020025514001315</mods:identifier>
   <mods:abstract>We introduce a methodology for sensitivity analysis of evidence variables in Gaussian Bayesian networks. Knowledge of the posterior probability distribution of the target variable in a Bayesian network, given a set of evidence, is desirable. However, this evidence is not always determined; in fact, additional information might be requested to improve the solution in terms of reducing uncertainty. In this study we develop a procedure, based on Shannon entropy and information theory measures, that allows us to prioritize information according to its utility in yielding a better result. Some examples illustrate the concepts and methods introduced.</mods:abstract>
   <mods:language>
      <mods:languageTerm>eng</mods:languageTerm>
   </mods:language>
   <mods:accessCondition type="useAndReproduction">restricted access</mods:accessCondition>
   <mods:titleInfo>
      <mods:title>Sensitivity to evidence in Gaussian Bayesian networks using mutual information</mods:title>
   </mods:titleInfo>
   <mods:genre>journal article</mods:genre>
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