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Infinite products and zero-one laws in categorical probability

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 Added by Eigil Rischel
 Publication date 2019
  fields
and research's language is English




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Markov categories are a recent category-theoretic approach to the foundations of probability and statistics. Here we develop this approach further by treating infinite products and the Kolmogorov extension theorem. This is relevant for all aspects of probability theory in which infinitely many random variables appear at a time. These infinite tensor products $bigotimes_{i in J} X_i$ come in t



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This paper reformulates a classical result in probability theory from the 1930s in modern categorical terms: de Finettis representation theorem is redescribed as limit statement for a chain of finite spaces in the Kleisli category of the Giry monad. This new limit is used to identify among exchangeable coalgebras the final one.
158 - David Jekel 2019
Let $(X_1,dots,X_m)$ be self-adjoint non-commutative random variables distributed according to the free Gibbs law given by a sufficiently regular convex and semi-concave potential $V$, and let $(S_1,dots,S_m)$ be a free semicircular family. We show that conditional expectations and conditional non-microstates free entropy given $X_1$, dots, $X_k$ arise as the large $N$ limit of the corresponding conditional expectations and entropy for the random matrix models associated to $V$. Then by studying conditional transport of measure for the matrix models, we construct an isomorphism $mathrm{W}^*(X_1,dots,X_m) to mathrm{W}^*(S_1,dots,S_m)$ which maps $mathrm{W}^*(X_1,dots,X_k)$ to $mathrm{W}^*(S_1,dots,S_k)$ for each $k = 1, dots, m$, and which also witnesses the Talagrand inequality for the law of $(X_1,dots,X_m)$ relative to the law of $(S_1,dots,S_m)$.
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