Joe Neeman: Gaussian isoperimetry and related topics II
The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Joe Neeman: Gaussian isoperimetry and related topics III
The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Joe Neeman: Gaussian isoperimetry and related topics I
The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
(PP 6.6) Geometric intuition for the multivariate Gaussian (part 1)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
(PP 6.7) Geometric intuition for the multivariate Gaussian (part 2)
How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.
From playlist Probability Theory
(PP 6.3) Gaussian coordinates does not imply (multivariate) Gaussian
An example illustrating the fact that a vector of Gaussian random variables is not necessarily (multivariate) Gaussian.
From playlist Probability Theory
(PP 6.4) Density for a multivariate Gaussian - definition and intuition
The density of a (multivariate) non-degenerate Gaussian. Suggestions for how to remember the formula. Mathematical intuition for how to think about the formula.
From playlist Probability Theory
(PP 6.5) Affine property, Constructing Gaussians, and Sphering
Any affine transformation of a (multivariate) Gaussian random variable is (multivariate) Gaussian. How to construct any (multivariate) Gaussian using an affine transformation of standard normals. How to "sphere" a Gaussian, i.e. transform it into a vector of independent standard normals.
From playlist Probability Theory
Minerva Lectures 2013 - Assaf Naor Talk 1: An introduction to the Ribe program
For more information, please see: http://www.math.princeton.edu/events/seminars/minerva-lectures/minerva-lecture-i-introduction-ribe-program
From playlist Minerva Lectures - Assaf Naor
Complex Stochastic Models and their Applications by Subhroshekhar Ghosh
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Seminar In the Analysis and Methods of PDE (SIAM PDE): Andrea R. Nahmod
Title: Gibbs measures and propagation of randomness under the flow of nonlinear dispersive PDE Date: Thursday, May 5, 2022, 11:30 am EDT Speaker: Andrea R. Nahmod, University of Massachusetts Amherst The COVID-19 pandemic and consequent social distancing call for online venues of research
From playlist Seminar In the Analysis and Methods of PDE (SIAM PDE)
Marek Biskup: Extreme points of two dimensional discrete Gaussian free field part 3
This lecture was held during winter school (01.19.2015 - 01.23.2015)
From playlist HIM Lectures 2015
Paul Hand - Signal Recovery with Generative Priors - IPAM at UCLA
Recorded 29 November 2022. Paul Hand of Northeastern University presents "Signal Recovery with Generative Priors" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Recovering images from very few measurements is an important task in imaging problems. Doing s
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Large deviations theory applied to large scale (...) - P. Reimberg - Workshop 1 - CEB T3 2018
Paulo Reimberg (IPhT) / 20.09.2018 Large deviations theory applied to large scale structure cosmology ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : ht
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Masha Gordina: Stochastic analysis and geometric functional inequalities
We will survey different methods of proving functional inequalities for hypoelliptic diffusions and the corresponding heat kernels. Some of these methods rely on geometric methods such as curvature-dimension inequalities (due to Baudoin-Garofalo), and some are probabilistic such as couplin
From playlist Trimester Seminar Series on the Interplay between High-Dimensional Geometry and Probability
Two manifestations of rigidity in point sets: forbidden regions... by Subhroshekhar Ghosh
PROGRAM :UNIVERSALITY IN RANDOM STRUCTURES: INTERFACES, MATRICES, SANDPILES ORGANIZERS :Arvind Ayyer, Riddhipratim Basu and Manjunath Krishnapur DATE & TIME :14 January 2019 to 08 February 2019 VENUE :Madhava Lecture Hall, ICTS, Bangalore The primary focus of this program will be on the
From playlist Universality in random structures: Interfaces, Matrices, Sandpiles - 2019
On the (unreasonable) effectiveness of compressive imaging – Ben Adcock, Simon Fraser University
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
(PP 6.2) Multivariate Gaussian - examples and independence
Degenerate multivariate Gaussians. Some sketches of examples and non-examples of Gaussians. The components of a Gaussian are independent if and only if they are uncorrelated.
From playlist Probability Theory
Andrew Thomas (7/1/2020): Functional limit theorems for Euler characteristic processes
Title: Functional limit theorems for Euler characteristic processes Abstract: In this talk we will present functional limit theorems for an Euler Characteristic process–the Euler Characteristics of a filtration of Vietoris-Rips complexes. Under this setup, the points underlying the simpli
From playlist AATRN 2020