(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
Transcendental Functions 3 Examples using Properties of Logarithms.mov
Examples using the properties of logarithms.
From playlist Transcendental Functions
Determining values of a variable at a particular percentile in a normal distribution
From playlist Unit 2: Normal Distributions
(New Version Available) Inverse Functions
New Version: https://youtu.be/q6y0ToEhT1E Define an inverse function. Determine if a function as an inverse function. Determine inverse functions. http://mathispower4u.wordpress.com/
From playlist Exponential and Logarithmic Expressions and Equations
Cumulative Distribution Functions and Probability Density Functions
This statistics video tutorial provides a basic introduction into cumulative distribution functions and probability density functions. The probability density function or pdf is f(x) which describes the shape of the distribution. It can tell you if you have a uniform, exponential, or nor
From playlist Statistics
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com
From playlist Differentiation of Hyperbolic Functions
Dyson Brownian motion, free fermions and connections to Random Matrix Theory-4 by Gregory Schehr
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021
Quantum chaos, random matrices and statistical physics (Lecture 03) by Arul Lakshminarayan
ORGANIZERS: Abhishek Dhar and Sanjib Sabhapandit DATE: 27 June 2018 to 13 July 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the ninth in the series. This is a pedagogical school, aimed at bridging the gap between masters-level courses and topics in
From playlist Bangalore School on Statistical Physics - IX (2018)
Spectral Statistics of Lévy Matrices - Patrick Lopatto
Analysis Seminar Topic: Spectral Statistics of Lévy Matrices Speaker: Patrick Lopatto Affiliation: Member, School of Mathematics Date: October 19, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Dyson Brownian motion, free fermions and connections to Random Matrix Theory by Gregory Schehr
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021
Top Eigenvalue of a Random Matrix: A tale of tails - Satya Majumdar
Speaker : Satya Majumdar (Directeur de Recherche in CNRS) Date and Time : 27 Jan 2012, 04:00 PM Venue : New Physical Sciences Building Auditorium, IISc, Bangalore Random matrices were first introduced by Wishart (1928) in the statistics literature to describe the covariance matrix of la
From playlist Top Eigenvalue of a Random Matrix: A tale of tails - Satya Majumdar
Camille Male: Freeness over the diagonal and the global fluctuations of Wigner matrice
Talk at the conference "Noncommutative geometry meets topological recursion", August 2021, University of Münster. Abstract: We characterize the limiting second order distributions of independent complex Wigner and deterministic matrices using Voiculescu’s notions of freeness over the diago
From playlist Noncommutative geometry meets topological recursion 2021
Transcendental Functions 14 Derivative of Natural Log of x Example 3.mov
More examples to work through.
From playlist Transcendental Functions
Benson Au: "Finite-rank perturbations of random band matrices via infinitesimal free probability"
Asymptotic Algebraic Combinatorics 2020 "Finite-rank perturbations of random band matrices via infinitesimal free probability" Benson Au - University of California, San Diego (UCSD) Abstract: Free probability provides a unifying framework for studying random multi-matrix models in the la
From playlist Asymptotic Algebraic Combinatorics 2020
Can We Observe the Quantum Origin of Primordial Perturbations? by Jerome Martin
PROGRAM: PHYSICS OF THE EARLY UNIVERSE - AN ONLINE PRECURSOR ORGANIZERS: Robert Brandenberger (McGill University, Montreal, Canada), Jerome Martin (Institut d'Astrophysique de Paris, France), Subodh Patil (Instituut-Lorentz for Theoretical Physics, Leiden, Netherlands) and L Sriramkumar (
From playlist Physics of The Early Universe - An Online Precursor
(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
Gergely Harcos - A glimpse at arithmetic quantum chaos
slides for this talk: https://www.msri.org/workshops/801/schedules/21771/documents/2987/assets/27969 Introductory Workshop: Analytic Number Theory February 06, 2017 - February 10, 2017 February 07, 2017 (11:00 AM PST - 12:00 PM PST) Speaker(s): Gergely Harcos (Central European Universit
From playlist Number Theory
(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
Dyson Brownian motion, free fermions and connections to Random Matrix Theory-2 by Gregory Schehr
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021