Graphical models | Markov networks
In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. The concept originates from the Sherrington–Kirkpatrick model. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic. Thus, a Markov network can represent certain dependencies that a Bayesian network cannot (such as cyclic dependencies); on the other hand, it can't represent certain dependencies that a Bayesian network can (such as induced dependencies). The underlying graph of a Markov random field may be finite or infinite. When the joint probability density of the random variables is strictly positive, it is also referred to as a Gibbs random field, because, according to the Hammersley–Clifford theorem, it can then be represented by a Gibbs measure for an appropriate (locally defined) energy function. The prototypical Markov random field is the Ising model; indeed, the Markov random field was introduced as the general setting for the Ising model. In the domain of artificial intelligence, a Markov random field is used to model various low- to mid-level tasks in image processing and computer vision. (Wikipedia).
(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 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 14.4) Hidden Markov models (HMMs) (part 1)
Definition of a hidden Markov model (HMM). Description of the parameters of an HMM (transition matrix, emission probability distributions, and initial distribution). Illustration of a simple example of a HMM.
From playlist Machine Learning
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
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
Diophantine properties of Markoff numbers - Jean Bourgain
Using available results on the strong approximation property for the set of Markoff triples together with an extension of Zagier’s counting result, we show that most Markoff numbers are composite. For more videos, visit http://video.ias.edu
From playlist Mathematics
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 (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
Nexus Trimester - Raymond Yeung (The Chinese University of Hong Kong) 3/3
Shannon's Information Measures and Markov Structures Raymond Yeung (The Chinese University of Hong Kong) February 18,2016 Abstract: Most studies of finite Markov random fields assume that the underlying probability mass function (pmf) of the random variables is strictly positive. With thi
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Nexus Trimester - Raymond Yeung (The Chinese University of Hong Kong) 1/3
Shannon's Information Measures and Markov Structures Raymond Yeung (The Chinese University of Hong Kong) February 18,2016 Abstract: Most studies of finite Markov random fields assume that the underlying probability mass function (pmf) of the random variables is strictly positive. With thi
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Marek Biskup: Extreme points of two dimensional discrete Gaussian free field part 3
This lecture was held during winter school (01.19.2015 - 01.23.2015)
From playlist HIM Lectures 2015
Nexus Trimester - Raymond Yeung (The Chinese University of Hong Kong) 2/3
Shannon's Information Measures and Markov Structures Raymond Yeung (The Chinese University of Hong Kong) February 18,2016 Abstract: Most studies of finite Markov random fields assume that the underlying probability mass function (pmf) of the random variables is strictly positive. With thi
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme
Markov processes and applications-5 by Hugo Touchette
PROGRAM : BANGALORE SCHOOL ON STATISTICAL PHYSICS - XII (ONLINE) ORGANIZERS : Abhishek Dhar (ICTS-TIFR, Bengaluru) and Sanjib Sabhapandit (RRI, Bengaluru) DATE : 28 June 2021 to 09 July 2021 VENUE : Online Due to the ongoing COVID-19 pandemic, the school will be conducted through online
From playlist Bangalore School on Statistical Physics - XII (ONLINE) 2021
Diffusive limits for random walks and diffusions with long memory – B. Tóth – ICM2018
Probability and Statistics Invited Lecture 12.3 Diffusive and super-diffusive limits for random walks and diffusions with long memory Bálint Tóth Abstract: We survey recent results of normal and anomalous diffusion of two types of random motions with long memory in ℝ^d or ℤ^d. The first
From playlist Probability and Statistics
Probabilistic Graphical Models (PGMs) In Python | Graphical Models Tutorial | Edureka
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka "Graphical Models" video answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Prob & Stats - Markov Chains (2 of 38) Markov Chains: An Introduction (Another Method)
Visit http://ilectureonline.com for more math and science lectures! In this video I will introduce an alternative method of solving the Markov chain. Next video in the Markov Chains series: http://youtu.be/ECrsoUtsKq0
From playlist iLecturesOnline: Probability & Stats 3: Markov Chains & Stochastic Processes
Max Fathi: Ricci curvature and functional inequalities for interacting particle systems
I will present a few results on entropic Ricci curvature bounds, with applications to interacting particle systems. The notion was introduced by M. Erbar and J. Maas and independently by A. Mielke. These curvature bounds can be used to prove functional inequalities, such as spectral gap bo
From playlist HIM Lectures: Follow-up Workshop to JTP "Optimal Transportation"