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dc.contributor.authorIrurozki, Ekhine
dc.contributor.authorCalvo Molinos, Borja ORCID
dc.contributor.authorLozano Alonso, José Antonio
dc.date.accessioned2014-01-22T09:06:03Z
dc.date.available2014-01-22T09:06:03Z
dc.date.issued2014-01-22T09:06:03Z
dc.identifier.urihttp://hdl.handle.net/10810/11239
dc.description.abstract[EN]The Mallows and Generalized Mallows models are compact yet powerful and natural ways of representing a probability distribution over the space of permutations. In this paper we deal with the problems of sampling and learning (estimating) such distributions when the metric on permutations is the Cayley distance. We propose new methods for both operations, whose performance is shown through several experiments. We also introduce novel procedures to count and randomly generate permutations at a given Cayley distance both with and without certain structural restrictions. An application in the field of biology is given to motivate the interest of this model.es
dc.language.isoenges
dc.relation.ispartofseriesEHU-KZAA-TR;2014-02
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectpermutationses
dc.subjectMallows modelses
dc.subjectsamplinges
dc.subjectlearninges
dc.subjectCayley distancees
dc.titleSampling and learning the Mallows and Generalized Mallows models under the Cayley distancees
dc.typeinfo:eu-repo/semantics/reportes
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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