Statistics | Probability distributions
The Kaniadakis Generalized Gamma distribution (or κ-Generalized Gamma distribution) is a four-parameter family of continuous statistical distributions, supported on a semi-infinite interval [0,∞), which arising from the Kaniadakis statistics. It is one example of a Kaniadakis distribution. The κ-Gamma is a deformation of the Generalized Gamma distribution. (Wikipedia).
From playlist Probability Distributions
Multivariate Gaussian distributions
Properties of the multivariate Gaussian probability distribution
From playlist cs273a
Number Theory 1.2 : The Gamma Function
In this video, I introduce the gamma function and show a few properties of it. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : None yet
From playlist Number Theory
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
(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning
(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
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
39 - The gamma distribution - an introduction
This video provides an introduction to the gamma distribution: describing it mathematically, discussing example situations which can be modelled using a gamma in Bayesian inference, then going on to discuss how its two parameters affect the shape of the distribution intuitively, and finall
From playlist Bayesian statistics: a comprehensive course
Lecture 2. Power law and scale-free networks.
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Samory Kpotufe: "Some New Insights On Transfer Learning"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Some New Insights On Transfer Learning" Samory Kpotufe - Columbia University, Statistics Abstract: Th
From playlist Machine Learning for Physics and the Physics of Learning 2019
Some Recent Insights on Transfer Learning - Samory Kpotufe
Seminar on Theoretical Machine Learning Topic: Some Recent Insights on Transfer Learning Speaker: Samory Kpotufe Affiliation: Columbia University; Member, School of Mathematics Date: March 31, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
27 - Prior predictive distribution: example Disease - 2
This video provides an example of deriving the prior predictive distribution (beta-binomial) for the case of a beta prior and a binomial likelihood. If you are interested in seeing more of the material on Bayesian statistics and inference, arranged into a playlist, please visit: https://w
From playlist Bayesian statistics: a comprehensive course
The dynamical Φ43Φ34 model: derivation of the renormalised equations - Martin Hairer
Martin Hairer University of Warwick March 5, 2014 For more videos, visit http://video.ias.edu
From playlist Mathematics
27 - Prior predictive distribution: example Disease - 1
This video provides an example of deriving the prior predictive distribution (beta-binomial) for the case of a beta prior and a binomial likelihood. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5
From playlist Bayesian statistics: a comprehensive course
42 - Prior predictive distribution for Gamma prior to Poisson likelihood
This video provides a derivation of the prior predictive distribution - a negative binomial - for when there is a Gamma prior to a Poisson likelihood. If you are interested in seeing more of the material on Bayesian statistics, arranged into a playlist, please visit: https://www.youtube.
From playlist Bayesian statistics: a comprehensive course
Petru Constantinescu - On the distribution of modular symbols and cohomology classes
Motivated by a series of conjectures of Mazur, Rubin and Stein, the study of the arithmetic statistics of modular symbols has received a lot of attention in recent years. In this talk, I will highlight several results about the distribution of modular symbols, including their Gaussian dist
From playlist École d'Été 2022 - Cohomology Geometry and Explicit Number Theory
Gamma Matrices in Action #2 | How to do Calculations with Gamma Matrices
In this video, we show you how to use Dirac’s gamma matrices to do calculations in relativistic #QuantumMechanics! If you want to read more about the gamma matrices, we can recommend the book „An Introduction to Quantum Field Theory“ by Michael Peskin and Daniel Schroeder, especially cha
From playlist Dirac Equation