4.5.5 Total Expectation: Video
MIT 6.042J Mathematics for Computer Science, Spring 2015 View the complete course: http://ocw.mit.edu/6-042JS15 Instructor: Albert R. Meyer License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.042J Mathematics for Computer Science, Spring 2015
(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
(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
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 4.4) Properties of expectation
(0:00) Properties of expectation. (6:17) Expectation rule. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
(0:00) Function of a random variable is a random variable. (1:43) Expectation rule. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Thm 1.10 - Probabilistic Version - part 06 - "Second Term"
Here we apply Jensen's inquality.
From playlist Theorem 1.10
EXTRA MATH 6A: introduction to likelihood theory
Forelæsning med Per B. Brockhoff. Kapitler:
From playlist DTU: Introduction to Statistics | CosmoLearning.org
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
L13.7 Derivation of the Law of Total Variance
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
L13.9 Section Means and Variances
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
Lec 23 | MIT 5.60 Thermodynamics & Kinetics, Spring 2008
Lecture 23: Colligative properties. View the complete course at: http://ocw.mit.edu/5-60S08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 5.60 Thermodynamics & Kinetics, Spring 2008
MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 View the complete course: http://ocw.mit.edu/6-041SCF13 Instructor: Jimmy Li License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
Transport in Perturbed Integrable Anharmonic Chains by Stefano Lepri
PROGRAM CLASSICAL AND QUANTUM TRANSPORT PROCESSES: CURRENT STATE AND FUTURE DIRECTIONS (ONLINE) ORGANIZERS: Alberto Imparato (University of Aarhus, Denmark), Anupam Kundu (ICTS-TIFR, India), Carlos Mejia-Monasterio (Technical University of Madrid, Spain) and Lamberto Rondoni (Polytechni
From playlist Classical and Quantum Transport Processes : Current State and Future Directions (ONLINE)2022
Lec 20 | MIT 5.60 Thermodynamics & Kinetics, Spring 2008
Lecture 20: Phase equilibria - two components. View the complete course at: http://ocw.mit.edu/5-60S08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 5.60 Thermodynamics & Kinetics, Spring 2008
L13.10 Mean of the Sum of a Random Number of Random Variables
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018
Jeffrey Galkowski: Quantum Sabine law for resonances in transmission problems
Abstract: We prove a quantum Sabine law for the location of resonances in transmission problems. In this talk, our main applications are to scattering by strictly convex, smooth, transparent obstacles and highly frequency dependent delta potentials. In each case, we give a sharp characteri
From playlist Partial Differential Equations
(PP 6.1) Multivariate Gaussian - definition
Introduction to the multivariate Gaussian (or multivariate Normal) distribution.
From playlist Probability Theory
L13.11 Variance of the Sum of a Random Number of Random Variables
MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
From playlist MIT RES.6-012 Introduction to Probability, Spring 2018