(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
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
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
PUSHING A GAUSSIAN TO THE LIMIT
Integrating a gaussian is everyones favorite party trick. But it can be used to describe something else. Link to gaussian integral: https://www.youtube.com/watch?v=mcar5MDMd_A Link to my Skype Tutoring site: dotsontutoring.simplybook.me or email dotsontutoring@gmail.com if you have ques
From playlist Math/Derivation Videos
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
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
(ML 19.1) Gaussian processes - definition and first examples
Definition of a Gaussian process. Elementary examples of Gaussian processes.
From playlist Machine Learning
(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
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
Gaussian Integral 7 Wallis Way
Welcome to the awesome 12-part series on the Gaussian integral. In this series of videos, I calculate the Gaussian integral in 12 different ways. Which method is the best? Watch and find out! In this video, I calculate the Gaussian integral by using a technique that is very similar to the
From playlist Gaussian Integral
Yair Shenfeld - The Brownian transport map - IPAM at UCLA
Recorded 09 February 2022. Yair Shenfeld of the Massachusetts Institute of Technology presents "The Brownian transport map" at IPAM's Calculus of Variations in Probability and Geometry Workshop. Abstract: The existence of Lipschitz transport maps between probability measures leads to tran
From playlist Workshop: Calculus of Variations in Probability and Geometry
Orli Herscovici - Kohler-Jobin Meets Ehrhard - IPAM at UCLA
Recorded 07 February 2022. Orli Herscovici of the Georgia Institute of Technology presents "Kohler-Jobin Meets Ehrhard: the sharp lower bound for the Gaussian principal frequency while the Gaussian torsional rigidity is fixed, via rearrangements" at IPAM's Calculus of Variations in Probab
From playlist Workshop: Calculus of Variations in Probability and Geometry
Amine Marrakchi: Ergodic theory of affine isometric actions on Hilbert spaces
The Gaussian functor associates to every orthogonal representation of a group G on a Hilbert space, a probability measure preserving action of G called a Gaussian action. This construction is a fundamental tool in ergodic theory and is the source of a large and interesting class of probabi
From playlist Probability and Statistics
ML Tutorial: Probabilistic Numerical Methods (Jon Cockayne)
Machine Learning Tutorial at Imperial College London: Probabilistic Numerical Methods Jon Cockayne (University of Warwick) February 22, 2017
From playlist Machine Learning Tutorials
Maxim Raginsky: "A mean-field theory of lazy training in two-layer neural nets"
High Dimensional Hamilton-Jacobi PDEs 2020 Workshop II: PDE and Inverse Problem Methods in Machine Learning "A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics" Maxim Raginsky - University of Illinois at Urbana-Cham
From playlist High Dimensional Hamilton-Jacobi PDEs 2020
Gaussian multiplicative chaos: applications and recent developments - Nina Holden
50 Years of Number Theory and Random Matrix Theory Conference Topic: Gaussian multiplicative chaos: applications and recent developments Speaker: Nina Holden Affiliation: ETH Zurich Date: June 22, 2022 I will give an introduction to Gaussian multiplicative chaos and some of its applicati
From playlist Mathematics
Galyna Livshyts - On a conjectural symmetric version of the Ehrhard inequality
Recorded 08 February 2022. Galyna Livshyts of the Georgia Institute of Technology presents "On a conjectural symmetric version of the Ehrhard inequality" at IPAM's Calculus of Variations in Probability and Geometry Workshop. Abstract: Ehrhard’s inequality is a sharp inequality about the Ga
From playlist Workshop: Calculus of Variations in Probability and Geometry