Exponentially modified Gaussian distribution

(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

(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

Multivariate Gaussian distributions

Properties of the multivariate Gaussian probability distribution

From playlist cs273a

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

(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

(ML 16.7) EM for the Gaussian mixture model (part 1)

Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.

From playlist Machine Learning

Exponential Growth Models

Introduces notation and formulas for exponential growth models, with solutions to guided problems.

From playlist Discrete Math

(PP 6.1) Multivariate Gaussian - definition

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

From playlist Probability Theory

Concentration of quantum states from quantum functional (...) - N. Datta - Workshop 2 - CEB T3 2017

Nilanjana Datta / 24.10.17 Concentration of quantum states from quantum functional and transportation cost inequalities Quantum functional inequalities (e.g. the logarithmic Sobolev- and Poincaré inequalities) have found widespread application in the study of the behavior of primitive q

From playlist 2017 - T3 - Analysis in Quantum Information Theory - CEB Trimester

The Fyodorov-Hiary-Keating conjecture - Paul Bourgade

Probability Seminar Topic: The Fyodorov-Hiary-Keating conjecture Speaker: Paul Bourgade Affiliation: New York University Date: March 17, 2023 Through the random matrix analogy, Fyodorov, Hiary and Keating conjectured very precisely the typical values of the Riemann zeta function in short

From playlist Mathematics

Phong NGUYEN - Recent progress on lattices's computations 2

This is an introduction to the mysterious world of lattice algorithms, which have found many applications in computer science, notably in cryptography. We will explain how lattices are represented by computers. We will present the main hard computational problems on lattices: SVP, CVP and

From playlist École d'Été 2022 - Cohomology Geometry and Explicit Number Theory

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

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

Large Deviations for the Largest Eigenvalue of Sub-Gaussian Wigner Matrices by Nicholas Cook

PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab

From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY

Radek Adamczak: Functional inequalities and concentration of measure II

Concentration inequalities are one of the basic tools of probability and asymptotic geo- metric analysis, underlying the proofs of limit theorems and existential results in high dimensions. Original arguments leading to concentration estimates were based on isoperimetric inequalities, whic

From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability

The Fyodorov-Hiary-Keating Conjecture - Louis-Pierre Arguin

50 Years of Number Theory and Random Matrix Theory Conference Topic: The Fyodorov-Hiary-Keating Conjecture Speaker: Louis-Pierre Arguin Affiliation: City University of New York June 22, 2022 In 2012, Fyodorov, Hiary & Keating and Fyodorov & Keating proposed a series of conjectures descri

From playlist Mathematics

Yuansi Chen: Recent progress on the KLS conjecture

Kannan, Lovász and Simonovits (KLS) conjectured in 1995 that the Cheeger isoperimetric coefficient of any log-concave density is achieved by half-spaces up to a universal constant factor. This conjecture also implies other important conjectures such as Bourgain’s slicing conjecture (1986)

From playlist Workshop: High dimensional measures: geometric and probabilistic aspects

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

Simulating data to understand analysis methods

This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.

From playlist NEW ANTS #1) Introductions

A Gentle Introduction to the Normal Probability Distribution (10-4)

A normal distribution models…pretty much everything! The Normal Curve is the idealized distribution, a smooth, continuous, symmetrical line. The normal curve is used with interval and ratio scales, continuous data. The most frequent score is the middle score, less frequent scores above and

From playlist Continuous Probability Distributions in Statistics (WK 10 - QBA 237)

Large deviations of Markov processes (Part - 1) by Hugo Touchette

Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst

From playlist Large deviation theory in statistical physics: Recent advances and future challenges