Entropy production during free expansion of an ideal gas by Subhadip Chakraborti
Abstract: According to the second law, the entropy of an isolated system increases during its evolution from one equilibrium state to another. The free expansion of a gas, on removal of a partition in a box, is an example where we expect to see such an increase of entropy. The constructi
From playlist Seminar Series
(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
Hamza Fawzi: "Sum-of-squares proofs of logarithmic Sobolev inequalities on finite Markov chains"
Entropy Inequalities, Quantum Information and Quantum Physics 2021 "Sum-of-squares proofs of logarithmic Sobolev inequalities on finite Markov chains" Hamza Fawzi - University of Cambridge Abstract: Logarithmic Sobolev inequalities play an important role in understanding the mixing times
From playlist Entropy Inequalities, Quantum Information and Quantum Physics 2021
(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
Physics - Thermodynamics 2: Ch 32.7 Thermo Potential (10 of 25) What is Entropy?
Visit http://ilectureonline.com for more math and science lectures! In this video explain and give examples of what is entropy. 1) entropy is a measure of the amount of disorder (randomness) of a system. 2) entropy is a measure of thermodynamic equilibrium. Low entropy implies heat flow t
From playlist PHYSICS 32.7 THERMODYNAMIC POTENTIALS
Equidistribution of Measures with High Entropy for General Surface Diffeomorphisms by Omri Sarig
PROGRAM : ERGODIC THEORY AND DYNAMICAL SYSTEMS (HYBRID) ORGANIZERS : C. S. Aravinda (TIFR-CAM, Bengaluru), Anish Ghosh (TIFR, Mumbai) and Riddhi Shah (JNU, New Delhi) DATE : 05 December 2022 to 16 December 2022 VENUE : Ramanujan Lecture Hall and Online The programme will have an emphasis
From playlist Ergodic Theory and Dynamical Systems 2022
Maximum Entropy Models for Texture Synthesis - Leclaire - Workshop 2 - CEB T1 2019
Arthur Leclaire (Univ. Bordeaux) / 14.03.2019 Maximum Entropy Models for Texture Synthesis. The problem of examplar-based texture synthesis consists in producing an image that has the same perceptual aspect as a given texture sample. It can be formulated as sampling an image which is 'a
From playlist 2019 - T1 - The Mathematics of Imaging
Regularized Functional Inequalities and Applications to Markov Chains by Pierre Youssef
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
Statistical Rethinking - Lecture 12
Lecture 12 - MCMC / Maximum Entropy - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Maxwell-Boltzmann distribution
Entropy and the Maxwell-Boltzmann velocity distribution. Also discusses why this is different than the Bose-Einstein and Fermi-Dirac energy distributions for quantum particles. My Patreon page is at https://www.patreon.com/EugeneK 00:00 Maxwell-Boltzmann distribution 02:45 Higher Temper
From playlist Physics
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
Sanjoy Mitter - Overview of variational approach to nonlinear filtering
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation
Optimal Mixing of Glauber Dynamics: Entropy Factorization via High-Dimensional Expan - Zongchen Chen
Computer Science/Discrete Mathematics Seminar I Topic: Optimal Mixing of Glauber Dynamics: Entropy Factorization via High-Dimensional Expansion Speaker: Zongchen Chen Affiliation: Georgia Institute of Technology Date: February 22, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
The Rule 54: Exactly solvable deterministic interacting model of transport by Tomaz Prosen
PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is
From playlist Thermalization, Many Body Localization And Hydrodynamics 2019
Entropy: The Heat Death of The Universe
Entropy: The Heat Death of The Universe - https://aperture.gg/heatdeath Sign up with Brilliant for FREE and start learning today: https://brilliant.org/aperture "Maximum Entropy" Hoodies — Available Now: https://aperture.gg/entropy As the arrow of time pushes us forward, each day the univ
From playlist Science & Technology 🚀
Sergio Verdu - Information Theory Today
Founded by Claude Shannon in 1948, information theory has taken on renewed vibrancy with technological advances that pave the way for attaining the fundamental limits of communication channels and information sources. Increasingly playing a role as a design driver, information theory is b
From playlist NOKIA-IHES Workshop
(ML 14.1) Markov models - motivating examples
Introduction to Markov models, using intuitive examples of applications, and motivating the concept of the Markov chain.
From playlist Machine Learning