Types of probability distributions | Statistical randomness
In statistics, an exchangeable sequence of random variables (also sometimes interchangeable) is a sequence X1, X2, X3, ... (which may be finitely or infinitely long) whose joint probability distribution does not change when the positions in the sequence in which finitely many of them appear are altered. Thus, for example the sequences both have the same joint probability distribution. It is closely related to the use of independent and identically distributed random variables in statistical models. Exchangeable sequences of random variables arise in cases of simple random sampling. (Wikipedia).
11 The definition of exchangeability
An introduction to the concept of exchangeability for sequences of random variables. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm For more information on econometric
From playlist Bayesian statistics: a comprehensive course
Prob & Stats - Random Variable & Prob Distribution (1 of 53) Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and gives an example of what is a random variable. Next video in series: http://youtu.be/aEB07VIIfKs
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
Introduction to Random Variables
Introduction to random variables and probability distribution functions. More free lessons at: http://www.khanacademy.org/video?v=IYdiKeQ9xEI
From playlist Statistics
Statistics: Ch 5 Discrete Random Variable (1 of 27) What is a Random Variable?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn a random variable is a variable which represents the outcome of a trial, an experiment, or an event. It is a specific n
From playlist STATISTICS CH 5 DISCRETE RANDOM VARIABLE
More Joint Continuous [Normal] Random Variables
I recently uploaded 200 videos that are much more concise with excellent graphics. Click the link in the upper right-hand corner of this video. It will take you to my youtube channel where videos are arranged in playlists. In this older video: explanation & word problem involving the ne
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
(PP 3.1) Random Variables - Definition and CDF
(0:00) Intuitive examples. (1:25) Definition of a random variable. (6:10) CDF of a random variable. (8:28) Distribution of a random variable. A playlist of the Probability Primer series is available here: http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
From playlist Probability Theory
Prob & Stats - Random Variable & Prob Distribution (2 of 53) Random Variable - Terminology Review
Visit http://ilectureonline.com for more math and science lectures! In this video I will define and reviews terminologies associated with random variables. Next video in series: http://youtu.be/ebP7x2zviBI
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
Exchangeable random arrays - Tim Austin
Conference on Graphs and Analysis Tim Austin June 7, 2012 More videos on http://video.ias.edu
From playlist Mathematics
Prob & Stats - Random Variable & Prob Distribution (4 of 53) Types of Random Variable
Visit http://ilectureonline.com for more math and science lectures! In this video I will define 2 types of random variables (discrete and continuous variables). Next video in series: http://youtu.be/mtt3h54aSkk
From playlist iLecturesOnline: Probability & Stats 2: Random Variable & Probability Distribution
From graph limits to higher order Fourier analysis – Balázs Szegedy – ICM2018
Combinatorics Invited Lecture 13.8 From graph limits to higher order Fourier analysis Balázs Szegedy Abstract: The so-called graph limit theory is an emerging diverse subject at the meeting point of many different areas of mathematics. It enables us to view finite graphs as approximation
From playlist Combinatorics
From playlist Contributed talks One World Symposium 2020
Emmanuel Candès: “The Knockoffs Framework: New Statistical Tools for Replicable Selections”
Green Family Lecture Series 2018 “The Knockoffs Framework: New Statistical Tools for Replicable Selections” Emmanuel Candès, Stanford University Abstract: A common problem in modern statistical applications is to select, from a large set of candidates, a subset of variables which are imp
From playlist Public Lectures
Persi Diaconis: Haar-distributed random matrices - in memory of Elizabeth Meckes
Elizabeth Meckes spent many years studying properties of Haar measure on the classical compact groups along with applications to high dimensional geometry. I will review some of her work and some recent results I wish I could have talked about with her.
From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability
An explanation of the relationship between exchangeability and the assumption that sequences are independent, and identically distributed (iid). If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5Yzjq
From playlist Bayesian statistics: a comprehensive course
Bénédicte Haas : Introduction aux processus de fragmentation 1/2
Résumé : Les processus de fragmentation sont des modèles aléatoires pour décrire l'évolution d'objets (particules, masses) sujets à des fragmentations successives au cours du temps. L'étude de tels modèles remonte à Kolmogorov, en 1941, et ils ont depuis fait l'objet de nombreuses recherc
From playlist Probability and Statistics
Christian Borgs: Graphons and graphexes as limits of sparse graphs - lecture 2
Abstract: Graphons and graphexes are limits of graphs which allow us to model and estimate properties of large-scale networks. In this pair of talks, we review the theory of dense graph limits, and give two alterative theories for limits of sparse graphs - one leading to unbounded graphons
From playlist Combinatorics
Nexus Trimester - Raymond Yeung (The Chinese University of Hong Kong) 1/3
Shannon's Information Measures and Markov Structures Raymond Yeung (The Chinese University of Hong Kong) February 18,2016 Abstract: Most studies of finite Markov random fields assume that the underlying probability mass function (pmf) of the random variables is strictly positive. With thi
From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme