(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
(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
(ML 18.4) Examples of Markov chains with various properties (part 1)
A very simple example of a Markov chain with two states, to illustrate the concepts of irreducibility, aperiodicity, and stationary distributions.
From playlist Machine Learning
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
Kolmogorov Complexity - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Markov Chains: n-step Transition Matrix | Part - 3
Let's understand Markov chains and its properties. In this video, I've discussed the higher-order transition matrix and how they are related to the equilibrium state. #markovchain #datascience #statistics For more videos please subscribe - http://bit.ly/normalizedNERD Markov Chain ser
From playlist Markov Chains Clearly Explained!
Kolmogorov Complexity Solution - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
7. Finite-state Markov Chains; The Matrix Approach
MIT 6.262 Discrete Stochastic Processes, Spring 2011 View the complete course: http://ocw.mit.edu/6-262S11 Instructor: Shan-Yuan Ho License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.262 Discrete Stochastic Processes, Spring 2011
Lecture 02: Markov Decision Processes
Second lecture on the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials
From playlist Reinforcement Learning Course: Lectures (Summer 2020)
34 Sundar - Invariant measures and ergodicity for stochastic Navier-Stokes equations
PROGRAM NAME :WINTER SCHOOL ON STOCHASTIC ANALYSIS AND CONTROL OF FLUID FLOW DATES Monday 03 Dec, 2012 - Thursday 20 Dec, 2012 VENUE School of Mathematics, Indian Institute of Science Education and Research, Thiruvananthapuram Stochastic analysis and control of fluid flow problems have
From playlist Winter School on Stochastic Analysis and Control of Fluid Flow
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
Markov Chains Clearly Explained! Part - 1
Let's understand Markov chains and its properties with an easy example. I've also discussed the equilibrium state in great detail. #markovchain #datascience #statistics For more videos please subscribe - http://bit.ly/normalizedNERD Markov Chain series - https://www.youtube.com/playl
From playlist Markov Chains Clearly Explained!
Asymptotic efficiency in high-dimensional covariance estimation – V. Koltchinskii – ICM2018
Probability and Statistics Invited Lecture 12.18 Asymptotic efficiency in high-dimensional covariance estimation Vladimir Koltchinskii Abstract: We discuss recent results on asymptotically efficient estimation of smooth functionals of covariance operator Σ of a mean zero Gaussian random
From playlist Probability and Statistics
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
Max Tschaikowski, Aalborg University
March 1, Max Tschaikowski, Aalborg University Lumpability for Uncertain Continuous-Time Markov Chains
From playlist Spring 2022 Online Kolchin seminar in Differential Algebra
Small noise limits in the stationary regimes by Vivek S Borkar
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
Regenerative sequences and processes and MCMC by Krishna Athreya
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
(ML 18.3) Stationary distributions, Irreducibility, and Aperiodicity
Definitions of the properties of Markov chains used in the Ergodic Theorem: time-homogeneous MC, stationary distribution of a MC, irreducible MC, aperiodic MC.
From playlist Machine Learning
Lewis Bowen - Classification of Bernoulli shifts
November 20, 2015 - Princeton University Bernoulli shifts over amenable groups are classified by entropy (this is due to Kolmogorov and Ornstein for Z and Ornstein-Weiss in general). A fundamental property is that entropy never increases under a factor map. This property is violated for no
From playlist Minerva Mini Course - Lewis Bowen