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Markov bases of conditional independence models for permutations

Villő Csiszár (2009)

Kybernetika

The L-decomposable and the bi-decomposable models are two families of distributions on the set S n of all permutations of the first n positive integers. Both of these models are characterized by collections of conditional independence relations. We first compute a Markov basis for the L-decomposable model, then give partial results about the Markov basis of the bi-decomposable model. Using these Markov bases, we show that not all bi-decomposable distributions can be approximated arbitrarily well by...

Mean mutual information and symmetry breaking for finite random fields

J. Buzzi, L. Zambotti (2012)

Annales de l'I.H.P. Probabilités et statistiques

G. Edelman, O. Sporns and G. Tononi have introduced the neural complexity of a family of random variables, defining it as a specific average of mutual information over subfamilies. We show that their choice of weights satisfies two natural properties, namely invariance under permutations and additivity, and we call any functional satisfying these two properties an intricacy. We classify all intricacies in terms of probability laws on the unit interval and study the growth rate of maximal intricacies...

Mixture decompositions of exponential families using a decomposition of their sample spaces

Guido F. Montúfar (2013)

Kybernetika

We study the problem of finding the smallest m such that every element of an exponential family can be written as a mixture of m elements of another exponential family. We propose an approach based on coverings and packings of the face lattice of the corresponding convex support polytopes and results from coding theory. We show that m = q N - 1 is the smallest number for which any distribution of N q ...

Multiplicative free square of the free Poisson measure and examples of free symmetrization

Melanie Hinz, Wojciech Młotkowski (2010)

Colloquium Mathematicae

We compute the moments and free cumulants of the measure ρ t : = π t ⊠ π t , where π t denotes the free Poisson law with parameter t > 0. We also compute free cumulants of the symmetrization of ρ t . Finally, we introduce the free symmetrization of a probability measure on ℝ and provide some examples.

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