36 - Population mean test score - normal prior and likelihood
This video provides an example of Bayesian inference for the case of a normal prior and normal likelihood. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfortunately,
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics probability - part 1
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Unfo
From playlist Bayesian statistics: a comprehensive course
30 - Normal prior and likelihood - known variance
Provides an introduction to the example which will be used to describe inference for the case of a normal likelihood, with known variance, and a normal prior distribution. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com
From playlist Bayesian statistics: a comprehensive course
35 - Normal prior and likelihood - posterior predictive distribution
This video provides a derivation of the normal posterior predictive distribution for the case of a normal prior distribution and likelihood. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4o
From playlist Bayesian statistics: a comprehensive course
What is a probability distribution?
An introduction to probability distributions - both discrete and continuous - via simple examples. 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
From playlist Bayesian statistics: a comprehensive course
Bayesian vs frequentist statistics
This video provides an intuitive explanation of the difference between Bayesian and classical frequentist statistics. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdiokWB9gEpm Un
From playlist Bayesian statistics: a comprehensive course
What the Heck is Bayesian Stats ?? : Data Science Basics
What's all the hype about Bayesian statistics? My Patreon : https://www.patreon.com/user?u=49277905
From playlist Bayesian Statistics
Bayesian vs frequentist statistics probability - part 2
This video provides a short introduction to the similarities and differences between Bayesian and Frequentist views on probability. If you are interested in seeing more of the material, arranged into a playlist, please visit: https://www.youtube.com/playlist?list=PLFDbGp5YzjqXQ4oE4w9GVWdi
From playlist Bayesian statistics: a comprehensive course
39 - The gamma distribution - an introduction
This video provides an introduction to the gamma distribution: describing it mathematically, discussing example situations which can be modelled using a gamma in Bayesian inference, then going on to discuss how its two parameters affect the shape of the distribution intuitively, and finall
From playlist Bayesian statistics: a comprehensive course
Bayes Billiards with Tom Crawford
Bayes' Theorem allows us to assign a probability to an unknown fact. Thomas Bayes himself described an experiment with a billiard table, which is brilliantly explained by Hannah Fry and Matt Parker here https://www.youtube.com/watch?v=7GgLSnQ48os Brian Cox and David Spiegelhalter did a 1
From playlist Collaborations
Lecture 10/16 : Combining multiple neural networks to improve generalization
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 10A Why it helps to combine models 10B Mixtures of Experts 10C The idea of full Bayesian learning 10D Making full Bayesian learning practical 10E Dropout: an efficient way to combine neural nets
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
The Surprisingly Effective Magic of Partial Pooling
My Patreon : https://www.patreon.com/user?u=49277905 Partial Pooling Blog Post : https://conductrics.com/prediction-pooling-and-shrinkage 0:00 Intro 7:00 Intuition 9:53 Bayesian Magic Icon References : Coffee shop icons created by smalllikeart - Flaticon https://www.flaticon.com/free-i
From playlist Bayesian Statistics
Supercharging Decision Making with Bayes
Bayesian Decision Theory is a fundamental statistical approach to the problem of pattern classification. It is considered as the ideal pattern classifier and often used as the benchmark for other algorithms because its decision rule automatically minimizes its loss function. PUBLICATION P
From playlist Machine Learning
Bayesian Statistics: An Introduction
See all my videos here: http://www.zstatistics.com/videos/ 0:00 Introduction 2:25 Frequentist vs Bayesian 5:55 Bayes Theorum 10:45 Visual Example 15:05 Bayesian Inference for a Normal Mean 24:30 Conjugate priors 32:55 Credible Intervals
From playlist Statistical Inference (7 videos)
Statistical Rethinking Fall 2017 - week04 lecture08
Week 04, lecture 08 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 6. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http://xcel
From playlist Statistical Rethinking Fall 2017
Statistical Learning: 8.6 Bayesian Additive Regression Trees
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
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 Bayesian approach, Bayes rule, posterior distribution, and non-informative priors. License: Creative Commons
From playlist MIT 18.650 Statistics for Applications, Fall 2016
Why not to be afraid of priors (too much), Paul-Christian Bürkner - Bayes@Lund 2018
More info about Bayes@Lund, including slides: https://bayesat.github.io/lund2018/bayes_at_lund_2018.html
From playlist Bayes@Lund 2018
Statistical Rethinking - Lecture 08
Lecture 08 - Model comparison (2) - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
How to run A/B Tests as a Data Scientist!
Let's learn about how & why you should use Bayesian Testing. And some advantages of the Bayesian approach over frequentist approach with REAL data/code. Note: Bayesian Appraoch isn't necessarily better in every way - it is another perspective of looking at data. CODE: https://github.com/a
From playlist A/B Testing