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Equivalent or absolutely continuous probability measures with given marginals

Patrizia Berti, Luca Pratelli, Pietro Rigo, Fabio Spizzichino (2015)

Dependence Modeling

Let (X,A) and (Y,B) be measurable spaces. Supposewe are given a probability α on A, a probability β on B and a probability μ on the product σ-field A ⊗ B. Is there a probability ν on A⊗B, with marginals α and β, such that ν ≪ μ or ν ~ μ ? Such a ν, provided it exists, may be useful with regard to equivalent martingale measures and mass transportation. Various conditions for the existence of ν are provided, distinguishing ν ≪ μ from ν ~ μ.

Exceptional directions for Sierpiński's nonmeasurable sets

B. Kirchheim, Tomasz Natkaniec (1992)

Fundamenta Mathematicae

In [2] the question was considered in how many directions can a nonmeasurable plane set behave even "better" than the classical one constructed by Sierpiński in [6], in the sense that any line in a given direction intersects the set in at most one point. We considerably improve these results and give a much sharper estimate for the size of the sets of those "better" directions.

Extremal solutions of a general marginal problem

Petra Linhartová (1991)

Commentationes Mathematicae Universitatis Carolinae

The characterization of extremal points of the set of probability measures with given marginals is given in the general context of a marginal system. The sets of marginal uniqueness are studied and an example is added to illustrate the theory.

Implicit Markov kernels in probability theory

Daniel Hlubinka (2002)

Commentationes Mathematicae Universitatis Carolinae

Having Polish spaces 𝕏 , 𝕐 and we shall discuss the existence of an 𝕏 × 𝕐 -valued random vector ( ξ , η ) such that its conditional distributions K x = ( η ξ = x ) satisfy e ( x , K x ) = c ( x ) or e ( x , K x ) C ( x ) for some maps e : 𝕏 × 1 ( 𝕐 ) , c : 𝕏 or multifunction C : 𝕏 2 respectively. The problem is equivalent to the existence of universally measurable Markov kernel K : 𝕏 1 ( 𝕐 ) defined implicitly by e ( x , K x ) = c ( x ) or e ( x , K x ) C ( x ) respectively. In the paper we shall provide sufficient conditions for the existence of the desired Markov kernel. We shall discuss some special solutions of the ( e , c ) - or ( e , C ) -problem and illustrate...

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