Gauss-Gordon Method (Gauss-Jordan Elimination analogy)
The Gauss-Gordon Method (Gauss-Jordan Elimination analogy) If you can follow a recipe, you can solve linear systems. This is because the Gauss-Jordan elimination method for solving linear systems is “algorithmic;” simply put, it just follows a prescribed set of steps. In this video, we
From playlist Linear Algebra
Introduction to additive combinatorics lecture 10.8 --- A weak form of Freiman's theorem
In this short video I explain how the proof of Freiman's theorem for subsets of Z differs from the proof given earlier for subsets of F_p^N. The answer is not very much: the main differences are due to the fact that cyclic groups of prime order do not have lots of subgroups, so one has to
From playlist Introduction to Additive Combinatorics (Cambridge Part III course)
Differential Equations | First Order Linear System of DEs.
We solve a nonhomogeneous system of first order linear differential equations using a strategy inspired from solving a single first order linear differential equation. http://www.michael-penn.net http://www.randolphcollege.edu/mathematics/
From playlist Systems of Differential Equations
Approximating Functions in a Metric Space
Approximations are common in many areas of mathematics from Taylor series to machine learning. In this video, we will define what is meant by a best approximation and prove that a best approximation exists in a metric space. Chapters 0:00 - Examples of Approximation 0:46 - Best Aproximati
From playlist Approximation Theory
Accelerating MCMC for Computationally Intensive Models by Natesh Pillai
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
Christian P. Robert: Bayesian computational methods
Abstract: This is a short introduction to the many directions of current research in Bayesian computational statistics, from accelerating MCMC algorithms, to using partly deterministic Markov processes like the bouncy particle and the zigzag samplers, to approximating the target or the pro
From playlist Probability and Statistics
Reduction of Order - Linear Second Order Homogeneous Differential Equations Part 2
This video explains how to apply the method of reduction of order to solve a linear second order homogeneous differential equations. Site: http://mathispower4u
From playlist Second Order Differential Equations: Reduction of Order
Reduction of Order - Linear Second Order Homogeneous Differential Equations Part 1
This video explains how to apply the method of reduction of order to solve a linear second order homogeneous differential equations. Site: http://mathispower4u
From playlist Second Order Differential Equations: Reduction of Order
Non-stationary Markow Processes: Approximations and Numerical Methods by Peter Glynn
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Expectation Maximization: how it works
Full lecture: http://bit.ly/EM-alg We run through a couple of iterations of the EM algorithm for a mixture model with two univariate Gaussians. We initialise the Gaussian means and variances with random values, then compute the posterior probabilities for each data point, and use the post
From playlist Mixture Models
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
Convolution Theorem: Fourier Transforms
Free ebook https://bookboon.com/en/partial-differential-equations-ebook Statement and proof of the convolution theorem for Fourier transforms. Such ideas are very important in the solution of partial differential equations.
From playlist Partial differential equations
Christian Robert : Markov Chain Monte Carlo Methods - Part 1
Abstract: In this short course, we recall the basics of Markov chain Monte Carlo (Gibbs & Metropolis sampelrs) along with the most recent developments like Hamiltonian Monte Carlo, Rao-Blackwellisation, divide & conquer strategies, pseudo-marginal and other noisy versions. We also cover t
From playlist Probability and Statistics
Statistical Rethinking 2022 Lecture 08 - Markov chain Monte Carlo
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: Intro: https://www.youtube.com/watch?v=E06X1NXRdR4 Skate1 vid: https://www.youtube.com/watch?v=GCr0EO41t8g Skate1 music: https://www.youtube.com/watch?v=o3WvAhOAoCg Skate2 vid: https://www.youtube
From playlist Statistical Rethinking 2022
Zeros of polynomials, decay of correlations, and algorithms by Piyush Srivastava
DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n
From playlist Statistical Physics of Machine Learning 2020
Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 2: Markov chain Monte Carlo
Bayesian inference and mathematical imaging - Part 2: Markov chain Monte Carlo Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underpinning Bayesi
From playlist Probability and Statistics
Dr Anthony Lee, University of Warwick
Bio Anthony Lee has been an Assistant Professor of Statistics at the University of Warwick since 2013. He received BSc. and MSc. degrees in Computer Science from the University of British Columbia, and a DPhil. in Statistics from the University of Oxford in 2011. He was a Centre for Resea
From playlist Short Talks
Professor Gareth Roberts: "New challenges in Computational Statistics"
The Turing Lectures: Statistics - Professor Gareth Roberts, University of Warwick “New challenges in Computational Statistics” Click the below timestamps to navigate the video. 00:00:09 Welcome by Professor Patrick Wolfe 00:01:44 Introduction by Professor Sofia Olhede 00:03:2
From playlist Turing Lectures
Solve a Bernoulli Differential Equation (Part 2)
This video provides an example of how to solve an Bernoulli Differential Equation. The solution is verified graphically. Library: http://mathispower4u.com
From playlist Bernoulli Differential Equations
Massoumeh Dashti: Bayesian methods for inverse problems - lecture 2
Recorded during the meeting "Data Assimilation and Model Reduction in High Dimensional Problems" the July 19, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone A kinetic description of a plasma in external and self-consistent fiel
From playlist Virtual Conference