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dc.contributor.authorIrurozki, Ekhine
dc.contributor.authorCalvo Molinos, Borja ORCID
dc.contributor.authorLozano Alonso, José Antonio
dc.date.accessioned2014-01-22T09:04:16Z
dc.date.available2014-01-22T09:04:16Z
dc.date.issued2014-01-22T09:04:16Z
dc.identifier.urihttp://hdl.handle.net/10810/11238
dc.description.abstract[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We describe methods for making inference, sampling and learning such distributions, some of which are novel in the literature. This paper also describes operations for permutations, with special attention in those related with the Kendall and Cayley distances and the random generation of permutations. These operations are of key importance for the efficient computation of the operations on distributions. These algorithms are implemented in the associated R package. Moreover, the internal code is written in C++.es
dc.language.isoenges
dc.relation.ispartofseriesEHU-KZAA-TR;2014-05
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.subjectpermutationses
dc.subjectMallows modelses
dc.subjectsamplinges
dc.subjectlearninges
dc.subjectR Projectes
dc.titleAn R package for permutations, Mallows and Generalized Mallows modelses
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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