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dc.contributor.authorSangüesa i Sole, Ramon
dc.contributor.authorCabós, Joan
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-03-16T18:32:41Z
dc.date.available2016-03-16T18:32:41Z
dc.date.issued1996-06
dc.identifier.citationSangüesa, R., Cabós, J., Cortes, C. "Probabilistic conditional independence: a similarity-based measure and its application to causal network learning". 1996.
dc.identifier.urihttp://hdl.handle.net/2117/84564
dc.description.abstractA new definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This new definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSSCAUSE is presented and its applications discussed and compared with analogous developments in possibilistic and probabilistic causal networks learning.
dc.format.extent19 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-96-37-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.otherSimilarity
dc.subject.otherPossibility distributions
dc.subject.otherPOSSCAUSE
dc.titleProbabilistic conditional independence: a similarity-based measure and its application to causal network learning
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.rights.accessOpen Access
local.identifier.drac646821
dc.description.versionPostprint (published version)
local.citation.authorSangüesa, R.; Cabós, J.; Cortes, C.


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