Exchangeable processes need not be mixtures of independent, identically distributed random variables

DOI10.1007/BF01886868zbMath0388.60036OpenAlexW2061082088MaRDI QIDQ4169990

Lester E. Dubins, David Freedman

Publication date: 1979

Published in: Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01886868

Mathematics Subject Classification ID

Stationary stochastic processes (60G10) Discrete-time Markov processes on general state spaces (60J05)

Related Items (16)

On a representation theorem for finitely exchangeable random vectors ⋮ De Finetti's theorem for abstract finite exchangeable sequences ⋮ Finitely additive mixtures of probability measures ⋮ Homeomorphisms and Fourier expansion ⋮ Quasi-invariant measures on commutative Lie groups of infinite product type ⋮ On the extendibility of finitely exchangeable probability measures ⋮ Bernstein operators and finite exchangeability ⋮ Duality and integral representation for excessive measures ⋮ Generalized symmetric polynomials and an approximate de Finetti representation ⋮ A mixture of independent identically distributed random variables need not admit a regular conditional probability given the exchangeable ?-field ⋮ SINGLETON CONDITIONS AND QUANTUM DE FINETTI'S THEOREM ⋮ Exponential approximations in completely regular topological spaces and extensions of Sanov's theorem ⋮ Almost sure weak convergence of random probability measures ⋮ Some exchangeable probabilities are singular with respect to all presentable probabilities ⋮ De Finetti's contribution to probability and statistics ⋮ Random assignment processes: strong law of large numbers and de Finetti theorem

Cites Work

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• De Finetti's theorem for Markov chains
• Finite exchangeable sequences
• Sufficient statistics and extreme points
• Measurable sets of measures
• Symmetric Measures on Cartesian Products
• On consistency of probability measures
• Groups of Automorphisms of Borel Spaces
• A note on exchangeable sequences
• Ergodic sets

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