Matrix Limits and Markov Chains
In this video I present a cool application of linear algebra in which I use diagonalization to calculate the eventual outcome of a mixing problem. This process is a simple example of what's called a Markov chain. Note: I just got a new tripod and am still experimenting with it; sorry if t
From playlist Eigenvalues
Prob & Stats - Markov Chains (8 of 38) What is a Stochastic Matrix?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a stochastic matrix. Next video in the Markov Chains series: http://youtu.be/YMUwWV1IGdk
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
(ML 14.7) Forward algorithm (part 1)
The Forward algorithm for hidden Markov models (HMMs).
From playlist Machine Learning
Dana Randall: Sampling algorithms and phase transitions
Markov chain Monte Carlo methods have become ubiquitous across science and engineering to model dynamics and explore large combinatorial sets. Over the last 20 years there have been tremendous advances in the design and analysis of efficient sampling algorithms for this purpose. One of the
From playlist Probability and Statistics
Brain Teasers: 10. Winning in a Markov chain
In this exercise we use the absorbing equations for Markov Chains, to solve a simple game between two players. The Zoom connection was not very stable, hence there are a few audio problems. Sorry.
From playlist Brain Teasers and Quant Interviews
Prob & Stats - Markov Chains: Method 2 (30 of 38) Basics***
Visit http://ilectureonline.com for more math and science lectures! In this video I will demonstrate the basics of method 2 of solving Markov chain problems. Next video in the Markov Chains series:
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
Stefan Thonhauser: PDMPs and Integrals PDMPs in risk theoryand QMC integration II
This talk will give an overview on the usage of piecewise deterministic Markov processes for risk theoretic modeling and the application of QMC integration in this framework. This class of processes includes several common risk models and their generalizations. In this field, many objects
From playlist Virtual Conference
(ML 14.3) Markov chains (discrete-time) (part 2)
Definition of a (discrete-time) Markov chain, and two simple examples (random walk on the integers, and a oversimplified weather model). Examples of generalizations to continuous-time and/or continuous-space. Motivation for the hidden Markov model.
From playlist Machine Learning
Stefan Thonhauser: 3PDMPs in risk theory and QMC integration III
VIRTUAL LECTURE Recording during the meeting "Quasi-Monte Carlo Methods and Applications " the November 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
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
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
Prob & Stats - Markov Chains (10 of 38) Regular Markov Chain
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a regular Markov chain. Next video in the Markov Chains series: http://youtu.be/DeG8MlORxRA
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
From playlist Contributed talks One World Symposium 2020
Prob & Stats - Markov Chains (9 of 38) What is a Regular Matrix?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a regular matrix. Next video in the Markov Chains series: http://youtu.be/loBUEME5chQ
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
Alex Blumenthal: Lyapunov exponents of the Navier-Stokes equations
CONFERENCE Recording during the thematic meeting : "Probabilistic Techniques for Random and Time-Dependent Dynamical Systems" the October 4, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks g
From playlist Probability and Statistics
Terry Lyons: Modelling Diffusive Systems
This lecture was held at The University of Oslo, May 24, 2007 and was part of the Abel Prize Lectures in connection with the Abel Prize Week celebrations. Program for the Abel Lectures 2007 1. “A Short History of Large Deviations” by Srinivasa Varadhan, Abel Laureate 2007, Courant
From playlist Abel Lectures
Christian P. Robert: The coordinate sampler: a non-reversible Gibbs-like MCMC sampler
VIRTUAL LECTURE Recording during the meeting "Quasi-Monte Carlo Methods and Applications " the November 05, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
Benoîte de Saporta: Stochastic modeling for population dynamics: simulation and inference - Part 1
The aim of this course is to present some examples of stochastic models suitable for population dynamics. The first part will introduce a class of continuous time models called piecewise deterministic Markov processes (PDMPs). Their trajectories are deterministic with jumps at random times
From playlist Probability and Statistics
This is a third lecture on "Stochastic Yang-Mills" by Professor Martin Hairer. For more materials and slides visit: https://sites.google.com/view/oneworld-pderandom/home
From playlist Summer School on PDE & Randomness
(ML 14.2) Markov chains (discrete-time) (part 1)
Definition of a (discrete-time) Markov chain, and two simple examples (random walk on the integers, and a oversimplified weather model). Examples of generalizations to continuous-time and/or continuous-space. Motivation for the hidden Markov model.
From playlist Machine Learning