Expectation Values in Quantum Mechanics
Expectation values in quantum mechanics are an important tool, which help us to mathematically describe measurements of quantum systems. You can think of expectation values as the average of all possible outcomes of a measurement, weighted by their respective probabilities. Contents: 00:
From playlist Quantum Mechanics, Quantum Field Theory
(PP 4.1) Expectation for discrete random variables
(0:00) Definition of expectation for discrete r.v.s. (4:17) Well-defined expectation. (8:15) E(X) may exist and be infinite. (10:58) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Derivation of Quantum Momentum
We go through the mathy steps of the derivation justifying the form of the momentum operator in quantum mechanics.
From playlist Quantum Mechanics Uploads
(ML 7.7.A2) Expectation of a Dirichlet random variable
How to compute the expected value of a Dirichlet distributed random variable.
From playlist Machine Learning
(PP 4.2) Expectation for random variables with densities
(0:00) Definition of expectation for r.v.s. with densities. (2:30) E(X) for a uniform random variable. (5:05) Well-defined expectation. (7:15) E(X) may exist and be infinite. (8:00) E(X) might fail to exist. A playlist of the Probability Primer series is available here: http://www.youtub
From playlist Probability Theory
Simon Barthelmé: The Expectation-Propagation algorithm: a tutorial - Part 1
Abstract: The Expectation-Propagation algorithm was introduced by Minka in 2001, and is today still one of the most effective algorithms for approximate inference. It is relatively difficult to implement well but in certain cases it can give results that are almost exact, while being much
From playlist Probability and Statistics
(ML 16.3) Expectation-Maximization (EM) algorithm
Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.
From playlist Machine Learning
Uncertainty and Propagation of Errors
A discussion of how to report experimental uncertainty, and how to calculate propagation of errors. Based on the nice video by paulcolor: https://youtu.be/V0ZRvvHfF0E, with some personal edits.
From playlist Experimental Physics
Derivation.3.Variance as an Expectation
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist Optional - Derivations
EmberConf 2018: Deep Dive on Ember Events by Marie Chatfield
EmberConf 2018: Deep Dive on Ember Events by Marie Chatfield Advance your Ember events knowledge from entry-level to expert in 30 minutes! Start with the basics of DOM events, learn the lifecycle of event listeners in Ember, then peek under the hood to understand how Ember handles clicks
From playlist EmberConf 2018
Probabilistic inverse problems (Lecture - 2) by Erkki Somersalo
DISCUSSION MEETING WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE) ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India) DATE: 25 October 2021 to 29 October 2021 VENUE: Online This week-long program will consist of several lectures by
From playlist Workshop on Inverse Problems and Related Topics (Online)
Large-scale structure response functions - F. Bernardeau - Workshop 1 - CEB T3 2018
Francis Bernardeau (IPhT, IAP) / 21.09.2018 Large-scale structure response functions ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Jianfeng Lu - Taming the dynamical sign problem in diagrammatic algorithms for open quantum systems
Recorded 31 March 2022. Jianfeng Lu of Duke University Mathematics presents "Taming the dynamical sign problem in diagrammatic algorithms for open quantum systems" at IPAM's Multiscale Approaches in Quantum Mechanics Workshop. Abstract: Numerical simulations for open quantum system dynamic
From playlist 2022 Multiscale Approaches in Quantum Mechanics Workshop
Cryptography and Network Security by Prof. D. Mukhopadhyay, Department of Computer Science and Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
From playlist Computer - Cryptography and Network Security
Front propagation in a nonlocal reaction-diffusion equation - Olga Turanova
Analysis Seminar Topic: Front propagation in a nonlocal reaction-diffusion equation Speaker: Olga Turanova Affiliation: University of California, Los Angeles; Visitor, School of Mathematics Date: March 21, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
The spelled-out intro to neural networks and backpropagation: building micrograd
This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school. Links: - micrograd on github: https://github.com/karpathy/micrograd - jupyter notebook
From playlist Neural Networks: Zero to Hero
Jens Marklof - Quantum Lorentz gas in the Boltzmann-Grad limit: random vs periodic
Jens Marklof (University of Bristol) Quantum Lorentz gas in the Boltzmann-Grad limit: random vs periodic. A major challenge in the kinetic theory of gases is to establish the convergence of the dynamics to a macroscopic transport process described by the appropriate kinetic equation. In
From playlist Large-scale limits of interacting particle systems
Quasi-biennial Oscillation of the Stratosphere: Role of the Equatorial... (Lecture 5) by B N Goswami
ICTS Summer Course 2022 (www.icts.res.in/lectures/sc2022bng) Title : Introduction to Indian monsoon Variability, Predictability, and Teleconnections Speaker : Professor B N Goswami (Cotton University) Date : 23rd April onwards every week o
From playlist Summer Course 2022: Introduction to Indian monsoon Variability, Predictability, and Teleconnections
Follow updates on Twitter: https://twitter.com/eigensteve This series discusses exponential growth, which is a ubiquitous phenomenon in science and engineering. This video will provide a high-level overview. Website: https://www.eigensteve.com/
From playlist Intro to Data Science