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On a representation theorem for finitely exchangeable random vectorsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2016 (English)In: Journal of Mathematical Analysis and Applications, ISSN 0022-247X, E-ISSN 1096-0813, Vol. 442, no 2, 703-714 p.Article in journal (Refereed) Published
##### Resource type

Text
##### Abstract [en]

##### Place, publisher, year, edition, pages

2016. Vol. 442, no 2, 703-714 p.
##### Keyword [en]

Signed measure, Measurable space, Point measure, Exchangeable, Symmetric, Homogeneous polynomial
##### National Category

Probability Theory and Statistics Mathematical Analysis
##### Identifiers

URN: urn:nbn:se:uu:diva-299017DOI: 10.1016/j.jmaa.2016.04.070ISI: 000377322700017OAI: oai:DiVA.org:uu-299017DiVA: diva2:948875
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##### Funder

Knut and Alice Wallenberg FoundationSwedish Research Council, 2013-4688
Available from: 2016-07-14 Created: 2016-07-13 Last updated: 2017-11-28Bibliographically approved

A random vector X = (X1, ... ,X-n) with the X-i taking values in an arbitrary measurable space (S, Sp) is exchangeable if its law is the same as that of (X-sigma(1), ... ,X-sigma(n)) for any permutation a. We give an alternative and shorter proof of the representation result (Jaynes [6] and Kerns and Szekely [9]) stating that the law of X is a mixture of product probability measures with respect to a signed mixing measure. The result is "finitistic" in nature meaning that it is a matter of linear algebra for finite S. The passing from finite S to an arbitrary one may pose some measure-theoretic difficulties which are avoided by our proof. The mixing signed measure is not unique (examples are given), but we pay more attention to the one constructed in the proof ("canonical mixing measure") by pointing out some of its characteristics. The mixing measure is, in general, defined on the space of probability measures on S; but for S =, one can choose a mixing measure on R-n.

doi
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