Bayes linear spaces
dc.contributor.author
dc.date.accessioned
2014-03-25T11:53:09Z
dc.date.available
2014-03-25T11:53:09Z
dc.date.issued
2010
dc.identifier.issn
1696-2281
dc.identifier.uri
dc.description.abstract
Linear spaces consisting of σ-finite probability measures and infinite measures (improper priors and likelihood functions) are defined. The commutative group operation, called perturbation, is the updating given by Bayes theorem; the inverse operation is the Radon-Nikodym derivative. Bayes spaces of measures are sets of classes of proportional measures. In this framework, basic notions of mathematical statistics get a simple algebraic interpretation. For example, exponential families appear as affine subspaces with their sufficient statistics as a basis. Bayesian statistics, in particular some well-known properties of conjugated priors and likelihood functions, are revisited and slightly extended
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Institut d´Estadística de Catalunya (Idescat)
dc.relation.isformatof
Reproducció digital del document publicat a: http://www.idescat.cat/sort/sort342/34.2.4.boogaart-etal.pdf
dc.relation.ispartof
SORT : statistics and operations research transactions, 2010, vol. 34, núm. 4, p. 201-222
dc.relation.ispartofseries
Articles publicats (D-IMA)
dc.rights
Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.uri
dc.subject
dc.title
Bayes linear spaces
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
Cap
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.idgrec
015158
dc.identifier.eissn
2013-8830