Compound probability distributions | Continuous distributions
In probability theory and statistics, the beta prime distribution (also known as inverted beta distribution or beta distribution of the second kind) is an absolutely continuous probability distribution. (Wikipedia).
Excel Beta Distribution (BETA.DIST)
How to use the BETA.DIST function in Excel for beta distribution cumulative probabilities. Three ways to format the function/
From playlist Excel for Statistics
(ML 7.5) Beta-Bernoulli model (part 1)
The Beta distribution is a conjugate prior for the Bernoulli. We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
(ML 7.6) Beta-Bernoulli model (part 2)
The Beta distribution is a conjugate prior for the Bernoulli. We derive the posterior distribution and the (posterior) predictive distribution under this model.
From playlist Machine Learning
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
The Beta Distribution : Data Science Basics
Estimating the probability of a probability. My Patreon : https://www.patreon.com/user?u=49277905 Shoe icons created by Freepik - Flaticon https://www.flaticon.com/free-icons/shoe
From playlist Data Science Basics
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
29 - Posterior predictive distribution: example Disease
This video provides an introduction to the concept of posterior predictive distributions, using the example of disease prevalence in a population. Here we consider the case of a beta prior and binomial likelihood; resulting in a beta-binomial posterior. If you are interested in seeing mo
From playlist Bayesian statistics: a comprehensive course
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution
Generalized Linear Model (Part B)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
Transformation and Weighting to correct model inadequacies (Part A)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
23. Generalized Linear Models (cont.)
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about strict concavity, optimization methods, quadratic approximation, Newton-Raphson method, and Fisher-scoring me
From playlist MIT 18.650 Statistics for Applications, Fall 2016
Generalized Linear Model (Part A)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
44 - Posterior predictive distribution a negative binomial for gamma prior to poisson likelihood
This video provides a derivation of the posterior predictive distribution - a negative binomial - for when there is a gamma prior to a Poisson likelihood. If you are interested in seeing more of the material on Bayesian statistics, arranged into a playlist, please visit: https://www.yout
From playlist Bayesian statistics: a comprehensive course
22. Generalized Linear Models (cont.)
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about log-likelihood function, link function, and canonical link, etc. License: Creative Commons BY-NC-SA More inf
From playlist MIT 18.650 Statistics for Applications, Fall 2016
The Universal Relation Between Exponents in First-Passage Percolation - Sourav Chatterjee
Sourav Chatterjee Courant Institute; NYU October 18, 2011 It has been conjectured in numerous physics papers that in ordinary first-passage percolation on integer lattices, the fluctuation exponent \chi and the wandering exponent \xi are related through the universal relation \chi=2\xi -1,
From playlist Mathematics
undergraduate machine learning 12: Bayesian learning
Introduction to Bayesian learning. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 at UBC by Nando de Freitas
From playlist undergraduate machine learning at UBC 2012
bayesian vs frequentist estimates and beta distribution
Use real data to demonstrate the concept of frequentist and bayesian approaches to estimate the probability distribution of a binary random variable using Beta distributions. Code at: https://gitlab.com/avilay/gyan
From playlist Summer of Math Exposition Youtube Videos