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Workshop in Convexity and Geometric Aspects of Harmonic Analysis

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https://hdl.handle.net/1853/70990
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Author: Paouris, Grigoris ×

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Now showing 1 - 3 of 3
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    Concentration and Convexity - Part 1
    (Georgia Institute of Technology, 2019-12-10) Paouris, Grigoris
    The Concentration of measure phenomenon is a fundamental tool of high dimensional probability and of Asymptotic Geometric Analysis. Independence or Isoperimetry are two typical reasons for the appearance of this phenomenon. In these talks I will introduce the phenomenon and I will show how High dimensional Geometry affects the concentration. In particular I will explain how "convexity" can be used to establish strong concentration inequalities in the Gauss space and how the "convexity" of the underline measure is responsible for deviation principles.
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    Concentration and Convexity - Part 2
    (Georgia Institute of Technology, 2019-12) Paouris, Grigoris
    The Concentration of measure phenomenon is a fundamental tool of high dimensional probability and of Asymptotic Geometric Analysis. Independence or Isoperimetry are two typical reasons for the appearance of this phenomenon. In these talks I will introduce the phenomenon and I will show how High dimensional Geometry affects the concentration. In particular I will explain how "convexity" can be used to establish strong concentration inequalities in the Gauss space and how the "convexity" of the underline measure is responsible for deviation principles.
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    Item
    Concentration and Convexity - Part 3
    (Georgia Institute of Technology, ) Paouris, Grigoris
    The Concentration of measure phenomenon is a fundamental tool of high dimensional probability and of Asymptotic Geometric Analysis. Independence or Isoperimetry are two typical reasons for the appearance of this phenomenon. In these talks I will introduce the phenomenon and I will show how High dimensional Geometry affects the concentration. In particular I will explain how "convexity" can be used to establish strong concentration inequalities in the Gauss space and how the "convexity" of the underline measure is responsible for deviation principles.