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Gaussian function

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form and with parametric extensionfor arbitrary real constants a, b and non-zero c. It is named after the mathematician Carl Friedrich Gauss. The graph of a Gaussian is a characteristic symmetric "bell curve" shape. The parameter a is the height of the curve's peak, b is the position of the center of the peak, and c (the standard deviation, sometimes called the Gaussian RMS width) controls the width of the "bell". Gaussian functions are often used to represent the probability density function of a normally distributed random variable with expected value μ = b and variance σ2 = c2. In this case, the Gaussian is of the form Gaussian functions are widely used in statistics to describe the normal distributions, in signal processing to define Gaussian filters, in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion equations and to define the Weierstrass transform. (Wikipedia).

Gaussian function
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(ML 19.2) Existence of Gaussian processes

Statement of the theorem on existence of Gaussian processes, and an explanation of what it is saying.

From playlist Machine Learning

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(ML 19.1) Gaussian processes - definition and first examples

Definition of a Gaussian process. Elementary examples of Gaussian processes.

From playlist Machine Learning

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(PP 6.1) Multivariate Gaussian - definition

Introduction to the multivariate Gaussian (or multivariate Normal) distribution.

From playlist Probability Theory

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(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

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Gaussian Integral 6 Gamma Function

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 properties of the gamma function, which

From playlist Gaussian Integral

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A PRG for Gaussian Polynomial Threshold Functions - Daniel Kane

Daniel Kane Harvard University March 15, 2011 We define a polynomial threshold function to be a function of the form f(x) = sgn(p(x)) for p a polynomial. We discuss some recent techniques for dealing with polynomial threshold functions, particular when evaluated on random Gaussians. We sho

From playlist Mathematics

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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

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(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

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(PP 6.8) Marginal distributions of a Gaussian

For any subset of the coordinates of a multivariate Gaussian, the marginal distribution is multivariate Gaussian.

From playlist Probability Theory

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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

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(ML 19.9) GP regression - introduction

Introduction to the application of Gaussian processes to regression. Bayesian linear regression as a special case of GP regression.

From playlist Machine Learning

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ML Tutorial: Gaussian Processes (Richard Turner)

Machine Learning Tutorial at Imperial College London: Gaussian Processes Richard Turner (University of Cambridge) November 23, 2016

From playlist Machine Learning Tutorials

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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

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06 Inference in Time Series

Slides and more information: https://mml-book.github.io/slopes-expectations.html

From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)

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03 Numerical Integration

Slides and more information: https://mml-book.github.io/slopes-expectations.html

From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)

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(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

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