(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
(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
(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
(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
A review of the notes common to all formations of a G chord.
From playlist Music Lessons
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Your Career
From playlist COMP0168 (2020/21)
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
Covariance: population vs. sample, and relationship to correlation (FRM T2-8)
[Here is my xls http://trtl.bz/2B9nqdO] Covariance is a measure of linear co-movement between variables. Independence implies zero covariance, but the converse is not necessarily true (because variables can be dependent in a non-linear way). Discuss this video in our FRM forum! https://trt
From playlist Quantitative Analysis (FRM Topic 2)
Zakhar Kabluchko: Random Polytopes, Lecture III
In these three lectures we will provide an introduction to the subject of beta polytopes. These are random polytopes defined as convex hulls of i.i.d. samples from the beta density proportional to (1 − ∥x∥2)β on the d-dimensional unit ball. Similarly, beta’ polytopes are defined as convex
From playlist Workshop: High dimensional spatial random systems
Robustness of G-Expectation under Knightian Uncertainty - Prof. Shige Peng
A workshop to commemorate the centenary of publication of Frank Knight’s "Risk, Uncertainty, and Profit" and John Maynard Keynes’ “A Treatise on Probability” This workshop is organised by the University of Oxford and supported by The Alan Turing Institute. For further details and regular
From playlist Uncertainty and Risk
(ML 3.3) Choosing f to minimize expected loss
Minimizing the expected loss more generally specializes to the case of minimizing it for each individual value of x. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Introduction to Probability and Statistics 131A. Lecture 10. Survey Sampling
UCI Math 131A: Introduction to Probability and Statistics (Summer 2013) Lec 10. Introduction to Probability and Statistics: Survey Sampling View the complete course: http://ocw.uci.edu/courses/math_131a_introduction_to_probability_and_statistics.html Instructor: Michael C. Cranston, Ph.D.
From playlist Math 131A: Introduction to Probability and Statistics
Stanford CS229M - Lecture 19: Mixture of Gaussians, spectral clustering
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: https://web.stanford.edu/class/stats214/ To view all online courses and programs offered by Stanford, visit: http://onli
From playlist Stanford CS229M: Machine Learning Theory - Fall 2021
(ML 16.9) EM for the Gaussian mixture model (part 3)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning