Multivariate kernel density estimation

(PP 6.4) Density for a multivariate Gaussian - definition and intuition

The density of a (multivariate) non-degenerate Gaussian. Suggestions for how to remember the formula. Mathematical intuition for how to think about the formula.

From playlist Probability Theory

(PP 6.1) Multivariate Gaussian - definition

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

From playlist Probability Theory

(PP 6.6) Geometric intuition for the multivariate Gaussian (part 1)

How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.

From playlist Probability Theory

(PP 6.7) Geometric intuition for the multivariate Gaussian (part 2)

How to visualize the effect of the eigenvalues (scaling), eigenvectors (rotation), and mean vector (shift) on the density of a multivariate Gaussian.

From playlist Probability Theory

Uncertainty Estimation via (Multi) Calibration

A Google TechTalk, presented by Aaron Roth, 2020/10/02 Paper Title: "Moment Multi-calibration and Uncertainty Estimation" ABSTRACT: We show how to achieve multi-calibrated estimators not just for means, but also for variances and other higher moments. Informally, this means that we can fi

From playlist Differential Privacy for ML

Maximum Likelihood Estimation Examples

http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in

From playlist Estimation and Detection Theory

Complex Stochastic Models and their Applications by Subhroshekhar Ghosh

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

Multivariable Calculus | Differentiability

We give the definition of differentiability for a multivariable function and provide a few examples. http://www.michael-penn.net https://www.researchgate.net/profile/Michael_Penn5 http://www.randolphcollege.edu/mathematics/

From playlist Multivariable Calculus

Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)

This video explains how to determine the kernel of a linear transformation.

From playlist Kernel and Image of Linear Transformation

Elisabeth Gassiat - Manifold Learning with Noisy Data

It is a common idea that high dimensional data (or features) may lie on low dimensional support making learning easier. In this talk, I will present a very general set-up in which it is possible to recover low dimensional non-linear structures with noisy data, the noise being totally unkno

From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023

Kernel Regression

From playlist Introduction to Neural Networks and Kernel Methods

Model-based clustering of high-dimensional data: Pitfalls & solutions - David Dunson

Virtual Workshop on Missing Data Challenges in Computation, Statistics and Applications Topic: Model-based clustering of high-dimensional data: Pitfalls & solutions Speaker: David Dunson Date: September 9, 2020 For more video please visit http://video.ias.edu

From playlist Mathematics

Score estimation with infinite-dimensional exponential families – Dougal Sutherland, UCL

Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can

From playlist Approximating high dimensional functions

Gaussian Processes - Part 2

From playlist COMP0168 (2020/21)

John Nolan: A measure of dependence for stable distributions

Abstract: A distance based measure of dependence is proposed for stable distributions that completely characterizes independence for a bivariate stable distribution. Properties of this measure are analyzed, and contrasted with the covariation and co-difference. A sample analog of the measu

From playlist Probability and Statistics

Didong Li

From playlist Plenary talks One World Symposium 2020

Lewis Marsh (8/3/20): Geometric and topological data analysis of enzyme kinetics

Title: Geometric and topological data analysis of enzyme kinetics Abstract: In this talk, we will mathematically study a differential equation model and generated data describing molecular dynamics of Extracellular Signal Regulated Kinase (ERK), which is known to be linked to human cancer

From playlist ATMCS/AATRN 2020

Statistical Rethinking 2022 Lecture 16 - Gaussian Processes

Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: https://www.youtube.com/watch?v=uYNzqgU7na4 Music: https://www.youtube.com/watch?v=kXuasY8pDpA Music: https://www.youtube.com/watch?v=eTtTB0nZdL0 Pause: https://www.youtube.com/watch?v=pxPdsqrQByM

From playlist Statistical Rethinking 2022

Functional Data Analysis Under Shape Constraints - Srivastava - Workshop 2 - CEB T1 2019

Anuj Srivastava (Florida state Univ.) / 13.03.2019 Functional Data Analysis Under Shape Constraints. (Joint work with Sutanoy Dasgupta, Ian Jermyn, and Debdeep Pati). We consider a subarea of functional data analysis, where functions of interest are constrained to have pre-determined sh

From playlist 2019 - T1 - The Mathematics of Imaging

Inverse normal with Z Table

Determining values of a variable at a particular percentile in a normal distribution

From playlist Unit 2: Normal Distributions