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arXiv:1705.00302math.DS

Measure concentration and the weak Pinsker property

Tim Austin

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Abstract

Let $(X,μ)$ be a standard probability space. An automorphism $T$ of $(X,μ)$ has the weak Pinsker property if for every $\varepsilon > 0$ it has a splitting into a direct product of a Bernoulli shift and an automorphism of entropy less than $\varepsilon$. This property was introduced by Thouvenot, who asked whether it holds for all ergodic automorphisms. This paper proves that it does. The proof actually gives a more general result. Firstly, it gives a relative version: any factor map from one ergodic automorphism to another can be enlarged by arbitrarily little entropy to become relatively Bernoulli. Secondly, using some facts about relative orbit equivalence, the analogous result holds for all free and ergodic measure-preserving actions of a countable amenable group. The key to this work is a new result about measure concentration. Suppose now that $μ$ is a probability measure on a finite product space $A^n$, and endow this space with its Hamming metric. We prove that $μ$ may be represented as a mixture of other measures in which (i) most of the weight in the mixture is on measures that exhibit a strong kind of concentration, and (ii) the number of summands is bounded in terms of the difference between the Shannon entropy of $μ$ and the combined Shannon entropies of its marginals.

arXiv comments
139p [v2:] Improvements for clarity and generality. Many typos fixed. Some terminology simplified, removed, or made more standard; notation adjusted accordingly. Old Conjecture C is now Theorem C. Old Part II now split into Parts II and III. Several e.g.s added to Part I, and several consequences and open problems to the end of Part III. [v3 and 4:] Small corrections following ref report
Journal reference
This is a pre-print of an article published in Publ.math.IHES (2018). The final authenticated version is available online at: https://doi.org/10.1007/s10240-018-0098-3
DOI
doi:10.1007/s10240-018-0098-3
Subjects
math.DSDynamical Systems math.MGMetric Geometry math.PRProbability

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