Causal Inference is a set of tools used to scientifically prove cause and effect, very commonly used in economics and medicine. This series will go over the basics that any data scientist should understand about causal inference - and point them to the tools they would need to perform it.
From playlist Causal Inference - The Science of Cause and Effect
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
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
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
Causation vs. Association - Causal Inference
In this video I talk about the difference between causation and association and explain each of these concepts through an example. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
Fundamental Question - Causal Inference
In this video, I define the fundamental question and problem of causal inference and use an example to further explain the concept.
From playlist Causal Inference - The Science of Cause and Effect
18. Bayesian Statistics (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 Bayesian confidence regions and Bayesian estimation. License: Creative Commons BY-NC-SA More information at
From playlist MIT 18.650 Statistics for Applications, Fall 2016
Ideal Experiment - Causal Inference
In this video, I give you more details about the fundamental question and the fundamental problem of causal inference with the help of an example (our ideal experiment).
From playlist Causal Inference - The Science of Cause and Effect
Assumptions - Causal Inference
In this video, I introduce the most important assumptions in casual inference that we use in order to avoid mistakes such as presuming association and causation to be one and the same, among others: - Positivity - SUTVA - Large Sample Size - Double Blinded - No Measurement Error - Exchan
From playlist Causal Inference - The Science of Cause and Effect
Sampling Distributions and Confidence Intervals (Part 1 of Intro to Statistics)
Intro to Statistics Part 1. This is part one of a short course on foundations of statistics. If you've never taken a statistics class before (or even if you have), this course will walk you through what you need to know. Check out my e-book, Sampling in Statistics, which covers everythin
From playlist Intro to Statistics
Rémi Bardenet: A tutorial on Bayesian machine learning: what, why and how - lecture 2
HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 25, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's
From playlist Mathematical Aspects of Computer Science
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)
Tim Sullivan: Brittleness and robustness of Bayesian inference for complex systems
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
William Wen: "Bayesian Statistics and its Application to Integrative Statistical Genomics"
Computational Genomics Summer Institute 2016 "Bayesian Statistics and its Application to Integrative Statistical Genomics" Xiaoquan (William) Wen, University of Michigan Institute for Pure and Applied Mathematics, UCLA July 18, 2016 For more information: http://computationalgenomics.bio
From playlist Computational Genomics Summer Institute 2016
We describe my favorite causal inference technique: the parametric G formula, my go-to for any standard observational causal inference problems
From playlist Causal Inference - The Science of Cause and Effect
Statistical Inference for Causal Inference - Causal Inference
In this video I explain the concept of statistical inference for causal inference through a realistic group ideal experiment example. Enjoy! Here's the link to my previous Statistical Inference Introduction video if you haven't watched it yet: https://youtu.be/fEGc8ZqveXM
From playlist Causal Inference - The Science of Cause and Effect
Mini-course on information by Rajaram Nityananda (Part 2)
Information processing in biological systems URL: https://www.icts.res.in/discussion_meeting/ipbs2016/ DATES: Monday 04 Jan, 2016 - Thursday 07 Jan, 2016 VENUE: ICTS campus, Bangalore From the level of networks of genes and proteins to the embryonic and neural levels, information at var
From playlist Information processing in biological systems