In statistical mechanics, the Gibbs algorithm, introduced by J. Willard Gibbs in 1902, is a criterion for choosing a probability distribution for the statistical ensemble of microstates of a thermodynamic system by minimizing the average log probability subject to the probability distribution pi satisfying a set of constraints (usually expectation values) corresponding to the known macroscopic quantities. in 1948, Claude Shannon interpreted the negative of this quantity, which he called information entropy, as a measure of the uncertainty in a probability distribution. In 1957, E.T. Jaynes realized that this quantity could be interpreted as missing information about anything, and generalized the Gibbs algorithm to non-equilibrium systems with the principle of maximum entropy and maximum entropy thermodynamics. Physicists call the result of applying the Gibbs algorithm the Gibbs distribution for the given constraints, most notably Gibbs's grand canonical ensemble for open systems when the average energy and the average number of particles are given. (See also partition function). This general result of the Gibbs algorithm is then a maximum entropy probability distribution. Statisticians identify such distributions as belonging to exponential families. (Wikipedia).
Discrete Math - 3.1.3 Sorting Algorithms
Bubble sort and insertion sort algorithms. Textbook: Rosen, Discrete Mathematics and Its Applications, 7e Playlist: https://www.youtube.com/playlist?list=PLl-gb0E4MII28GykmtuBXNUNoej-vY5Rz
From playlist Discrete Math I (Entire Course)
Build a Heap - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
The discrete-time Fourier transform
The Fourier transform is arguably the most important algorithm in signal processing and communications technology (not to mention neural time series data analysis!). This video provides an in-depth, step-by-step explanation of how the Fourier transform works. The video uses files you can
From playlist OLD ANTS #2) The discrete-time Fourier transform
(ML 14.6) Forward-Backward algorithm for HMMs
The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).
From playlist Machine Learning
Introduction to the z-Transform
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introduces the definition of the z-transform, the complex plane, and the relationship between the z-transform and the discrete-time Fourier transfor
From playlist The z-Transform
Centrality - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
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
Bayesian Networks 2 - Forward-Backward | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2ZszFms Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associa
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Greedy Algorithm | What Is Greedy Algorithm? | Introduction To Greedy Algorithms | Simplilearn
This video on the Greedy Algorithm will acquaint you with all the fundamentals of greedy programming paradigm. In this tutorial, you will learn 'What Is Greedy Algorithm?' with the help of suitable examples. And finally, you will also discover few important applications of greedy algorithm
From playlist Data Structures & Algorithms [2022 Updated]
The Fourier Transform and Derivatives
This video describes how the Fourier Transform can be used to accurately and efficiently compute derivatives, with implications for the numerical solution of differential equations. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow
From playlist Fourier
Markov Networks 2 - Gibbs Sampling | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
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
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
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
Discrete Math - 3.1.2 Searching Algorithms
Linear search and binary search algorithms. Textbook: Rosen, Discrete Mathematics and Its Applications, 7e Playlist: https://www.youtube.com/playlist?list=PLl-gb0E4MII28GykmtuBXNUNoej-vY5Rz
From playlist Discrete Math I (Entire Course)
Topic Models: Variational Inference for Latent Dirichlet Allocation (with Xanda Schofield)
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check out the whole course: https://sites.google.com/umd.edu/2021cl1webpage/ (Including homeworks and reading.) Xanda's Webpage: https://www.cs.hmc.edu/~xanda
From playlist Computational Linguistics I
Exploring the random landscapes of inference (Lecture 1) by Gérard Ben Arous
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
WHAT IS THE SQUARE ROOT ALGORITHM? How does the square root algorithm work and why | Nathan Dalaklis
I go ahead and answer the question of 'what is the square root algorithm'. Before we look at this one of many algorithms from real analysis. I'll go through a sample computation and run through the square root algorithm steps without a calculator. After looking at the square root process a
From playlist The New CHALKboard