Kaniadakis Gaussian distribution

Multivariate Gaussian distributions

Properties of the multivariate Gaussian probability distribution

From playlist cs273a

(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

(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

(ML 7.9) Posterior distribution for univariate Gaussian (part 1)

Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.

From playlist Machine Learning

(ML 7.10) Posterior distribution for univariate Gaussian (part 2)

Computing the posterior distribution for the mean of the univariate Gaussian, with a Gaussian prior (assuming known prior mean, and known variances). The posterior is Gaussian, showing that the Gaussian is a conjugate prior for the mean of a Gaussian.

From playlist Machine Learning

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

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

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

Gaussian/Normal Distributions

In this video we discuss the Gaussian (AKA Normal) probability distribution function. We show how it relates to the error function (erf) and discuss how to use this distribution analytically and numerically (for example when analyzing real-life sensor data or performing simulation of stoc

From playlist Probability

Gaussian Processes - Part 1

From playlist COMP0168 (2020/21)

Inference in Time Series

From playlist COMP0168 (2020/21)

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)

Lecture 15.2 — Anomaly Detection | Gaussian Distribution — [ Machine Learning | Andrew Ng ]

From playlist Machine Learning — Andrew Ng, Stanford University [FULL COURSE]

Gaussian approximations in smoothers and filters... - Morzfeld - Workshop 2 - CEB T3 2019

Morzfeld (U Arizona, USA) / 13.11.2019 Gaussian approximations in smoothers and filters for data assimilation ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincar

From playlist 2019 - T3 - The Mathematics of Climate and the Environment

Stanford CS229: Machine Learning | Summer 2019 | Lecture 9 - Bayesian Methods - Parametric & Non

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptRUmB Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html

From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

05 Data Analytics: Parametric Distributions

Lecture on parametric distributions, examples and applications. Follow along with the demonstration workflows in Python: o. Interactive visualization of parametric distributions: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_ParametricDistributions.ipynb o.

From playlist Data Analytics and Geostatistics

Machine learning - Maximum likelihood and linear regression

Maximum likelihood and linear regression. Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas

From playlist Machine Learning 2013

Machine Learning Lecture 26 "Gaussian Processes" -Cornell CS4780 SP17

Cornell class CS4780. (Online version: https://tinyurl.com/eCornellML ) GPyTorch GP implementatio: https://gpytorch.ai/ Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote15.html Small corrections: Minute 14: it should be P(y,w|x,D) and not P(y|x,w,D) sorry

From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"

(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