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dc.contributor.authorEgozcue, Juan-José
dc.contributor.authorPawlowsky-Glahn, Vera
dc.date.accessioned2020-02-24T16:10:24Z
dc.date.available2020-02-24T16:10:24Z
dc.date.issued2018-12-21
dc.identifier.citationEgozcue, J.; Pawlowsky-Glahn, V. Evidence functions: a compositional approach to information. "SORT", 21 Desembre 2018, vol. 1, p. 101-124.
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2117/178505
dc.description.abstractThe discrete case of Bayes’ formula is considered the paradigm of information acquisition. Prior and posterior probability functions, as well as likelihood functions, called evidence functions, are compositions following the Aitchison geometry of the simplex, and have thus vector character. Bayes’ formula becomes a vector addition. The Aitchison norm of an evidence function is introduced as a scalar measurement of information. A fictitious fire scenario serves as illustration. Two different inspections of affected houses are considered. Two questions are addressed: (a) which is the information provided by the outcomes of inspections, and (b) which is the most informative inspection.
dc.format.extent24 p.
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherEvidence function
dc.subject.otherBayes’ formula
dc.subject.otherAitchison geometry
dc.subject.othercompositions
dc.subject.otherorthonormal basis
dc.subject.othersimplex
dc.subject.otherscalar information
dc.titleEvidence functions: a compositional approach to information
dc.typeArticle
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60A Foundations of probability theory
dc.subject.amsClassificació AMS::60 Probability theory and stochastic processes::60E Distribution theory
dc.subject.amsClassificació AMS::62 Statistics::62E Distribution theory
dc.rights.accessOpen Access
local.citation.publicationNameSORT
local.citation.volume1
local.citation.startingPage101
local.citation.endingPage124


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain