In mathematics, more precisely in measure theory, a measure on the real line is called a discrete measure (in respect to the Lebesgue measure) if it is concentrated on an at most countable set. The support need not be a discrete set. Geometrically, a discrete measure (on the real line, with respect to Lebesgue measure) is a collection of point masses. (Wikipedia).
Introduction to Discrete and Continuous Functions
This video defines and provides examples of discrete and continuous functions.
From playlist Introduction to Functions: Function Basics
This video explains what is taught in discrete mathematics.
From playlist Mathematical Statements (Discrete Math)
Introduction to Discrete and Continuous Variables
This video defines and provides examples of discrete and continuous variables.
From playlist Introduction to Functions: Function Basics
Discrete Populations Mean, Variance and Standard Deviation
Discrete Populations Mean, Variance and Standard Deviation
From playlist Exam 1 material
DISCRETE Random Variables: Finite and Infinite Distributions (9-2)
A Discrete Random Variable is any outcome of a statistical experiment that takes on discrete (i.e., separate and distinct) numerical values. Discrete outcomes: all potential outcomes numerical values are integers (i.e., whole numbers). They cannot be negative. Using an example of tests in
From playlist Discrete Probability Distributions in Statistics (WK 9 - QBA 237)
Discrete Data and Continuous Data
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Discrete Data and Continuous Data
From playlist Statistics
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
More Standard Deviation and Variance of Joint Discrete Random Variables
Further example and understanding of Joint Discrete random variables and their standard deviation and variance
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
Jan Maas : Gradient flows and Ricci cuevature in discrete and quantum probability
Recording during the thematic meeting : "Geometrical and Topological Structures of Information" the August 28, 2017 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent
From playlist Geometry
Alisa Knizel: Log-gases on a quadratic lattice via discrete loop equations
We study a general class of log-gas ensembles on a quadratic lattice. Using a variational principle we prove that the corresponding empirical measures satisfy a law of large numbers and that their global fluctuations are Gaussian with a universal covariance. We apply our general results to
From playlist Jean-Morlet Chair - Grava/Bufetov
2020.07.09 Ronen Eldan - Localization and concentration of measures on the discrete hypercube (1/2)
For a probability measure $\mu$ on the discrete hypercube, we are interested in finding sufficient conditions under which $\mu$ either (a) Exhibits concentration (either in the sense of Lipschitz functions, or in a stronger sense such as a Poincare inequality), or (b) Can be decomposed as
From playlist One World Probability Seminar
This statistics video tutorial explains the difference between continuous data and discrete data. It gives plenty of examples and practice problems with graphs included. My Website: https://www.video-tutor.net Patreon Donations: https://www.patreon.com/MathScienceTutor Amazon Store: h
From playlist Statistics
Probability Theory - Part 3 - Discrete vs. Continuous Case [dark version]
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From playlist Probability Theory [dark version]
(PP 3.2) Types of Random Variables
(0:00) Discrete random variables. (1:08) Random variables with densities. (3:35) Decomposition into discrete, absolutely continuous, and singular continuous parts. (7:12) Relationship with discrete & density-type random variables. A playlist of the Probability Primer series is avai
From playlist Probability Theory
From playlist Contributed talks One World Symposium 2020
Jan Maas: Optimal transport methods for discrete and quantum systems (part 1)
Optimal transport has become a powerful tool to attack non-smooth problems in analysis and geometry. A key role is played by the 2-Wasserstein metric, which induces a rich geometric structure on the space of probability measures. This structure allows to obtain gradient flow structures for
From playlist HIM Lectures 2015
More Help with Expected Value of Discrete Random Variables
Additional insight into calculating the mean [expected vale] of joint discrete random variables
From playlist Unit 6 Probability B: Random Variables & Binomial Probability & Counting Techniques
Probability Theory - Part 3 - Discrete vs. Continuous Case
Support the channel on Steady: https://steadyhq.com/en/brightsideofmaths Or support me via PayPal: https://paypal.me/brightmaths Or via Ko-fi: https://ko-fi.com/thebrightsideofmathematics Or via Patreon: https://www.patreon.com/bsom Or via other methods: https://thebrightsideofmathematics.
From playlist Probability Theory
