The Normal Distribution (1 of 3: Introductory definition)
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From playlist The Normal Distribution
OCR MEI Statistics 2 2.01 Introducing the Poisson Distribution
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From playlist [OLD SPEC] TEACHING OCR MEI STATISTICS 2 (S2)
(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning
The Normal Distribution, Clearly Explained!!!
The normal, or Gaussian, distribution is the most common distribution in all of statistics. Here I explain the basics of how these distributions are created and how they should be interpreted. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/
From playlist StatQuest
Robert Lemke Oliver, Upper bounds on number fields
VaNTAGe Seminar, July 12, 2022 License: CC-BY-NC-SA Links to some of the references mentioned in the talk: Anderson,Gafni,Hughes,Lemke Oliver,Lowry-Duda,Thorne,Wang,Zhang (2022): https://arxiv.org/abs/2204.01651 Bhargava,Shankar,Wang (2022): https://arxiv.org/abs/2204.01331 Ellenberg,V
From playlist Arithmetic Statistics II
The Selberg Sieve and Large Sieve (Lecture 4) by Satadal Ganguly
Program Workshop on Additive Combinatorics ORGANIZERS: S. D. Adhikari and D. S. Ramana DATE: 24 February 2020 to 06 March 2020 VENUE: Madhava Lecture Hall, ICTS Bangalore Additive combinatorics is an active branch of mathematics that interfaces with combinatorics, number theory, ergod
From playlist Workshop on Additive Combinatorics 2020
The Large Sieve (Lecture 3) by Satadal Ganguly
Program Workshop on Additive Combinatorics ORGANIZERS: S. D. Adhikari and D. S. Ramana DATE: 24 February 2020 to 06 March 2020 VENUE: Madhava Lecture Hall, ICTS Bangalore Additive combinatorics is an active branch of mathematics that interfaces with combinatorics, number theory, ergod
From playlist Workshop on Additive Combinatorics 2020
37 - The Poisson distribution - an introduction - 1
This video provides an introduction to the Poisson distribution, providing a definition, discussing example situations which might be modelled adequately using this distribution, deriving its mean, providing simulations in Matlab which demonstrate its shape, discussing how it can be used t
From playlist Bayesian statistics: a comprehensive course
19 - Beta distribution - an introduction
This video provides an introduction to the beta distribution; giving its definition, explaining why we may use it, and the range of beliefs that can be described by this versatile distribution. If you are interested in seeing more of the material, arranged into a playlist, please visit: h
From playlist Bayesian statistics: a comprehensive course
Population Distribution versus Sampling Distribution
This video is aimed at describing the difference between population distribution and sampling distribution. The intended audience is students taking an introductory statistics reasoning class. The software being used is StatsCrunch, which is an online teaching tool for statistics. Prentice
From playlist Prob and Stats
Poisson Distribution EXPLAINED!
http://www.zstatistics.com/videos/ 0:25 Quick rundown 2:15 Assumptions underlying the Poisson distribution 3:08 Probability Mass Function calculation 5:14 Cumulative Distribution Function calculation 6:29 Visualisation of the Poisson distribution 7:25 Practice QUESTION!
From playlist Distributions (10 videos)
Statistics - 5.3 The Poisson Distribution
The Poisson distribution is used when we know a mean number of successes to expect in a given interval. We will learn what values we need to know and how to calculate the results for probabilities of exactly one value or for cumulative values. Power Point: https://bellevueuniversity-my
From playlist Applied Statistics (Entire Course)
05 Data Analytics: Parametric Distributions
Lecture on parametric distributions, examples and applications. Follow along with the demonstration workflows in Python: o. Interactive visualization of parametric distributions: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/Interactive_ParametricDistributions.ipynb o.
From playlist Data Analytics and Geostatistics
Continuous Distributions: Beta and Dirichlet Distributions
Video Lecture from the course INST 414: Advanced Data Science at UMD's iSchool. Full course information here: http://www.umiacs.umd.edu/~jbg/teaching/INST_414/
From playlist Advanced Data Science
Lecture 10 - Statistical Distributions
This is Lecture 10 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www
From playlist CSE519 - Data Science Fall 2016
Central Limit Theorem - Sampling Distribution of Sample Means - Stats & Probability
This statistics video tutorial provides a basic introduction into the central limit theorem. It explains that a sampling distribution of sample means will form the shape of a normal distribution regardless of the shape of the population distribution if a large enough sample is taken from
From playlist Statistics
From playlist STAT 200 Video Lectures
QRM 4-2: The Fisher-Tippett and the Pickands-Balkema-de Haan Theorems
Welcome to Quantitative Risk Management (QRM). It is time to discuss the two fundamental theorems of EVT. We will give the necessary information, for their interpretation and use, but we will skip the proofs. Most of all, we will try to connect the two theorems, which give us extremely st
From playlist Quantitative Risk Management
Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
https://www.patreon.com/ProfessorLeonard Statistics Lecture 6.3: Applications of the Standard Normal Distribution. Using z-score, Standard Score
From playlist Statistics (Full Length Videos)
Python for Data Analysis: Probability Distributions
This video covers the basics of working with probability distributions in Python, including the uniform, normal, binomial, geometric, exponential and Poisson distributions. It also includes a discussion of random number generation and setting the random seed. Subscribe: ► https://www.yout
From playlist Python for Data Analysis