Series
Workshop in Convexity and Geometric Aspects of Harmonic Analysis

Series Type
Event Series
Description
Associated Organization(s)
Associated Organization(s)
Organizational Unit
Organizational Unit

Publication Search Results

Now showing 1 - 3 of 3
Thumbnail Image
Item

Concentration and Convexity - Part 1

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.

Thumbnail Image
Item

Concentration and Convexity - Part 2

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.

Thumbnail Image
Item

Concentration and Convexity - Part 3

, 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.