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ISIPTA 2017

Exchangeable Choice Functions

Conference Paper Artificial Intelligence · Imprecise Probability · Uncertainty in Artificial Intelligence

Abstract

We investigate how to model exchangeability with choice functions. Exchangeability is a structural assessment on a sequence of uncertain variables. We show how such assessments constitute a special kind of indifference assessment, and how this idea leads to a counterpart of de Finetti’s Representation Theorem, both in a finite and a countable context.

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Context

Venue
International Symposium on Imprecise Probabilities: Theories and Applications
Archive span
2017-2025
Indexed papers
59
Paper id
295004285176223584
v2026.09.13