Today's video is the first of a multi-part series on Bayesian methods! We'll derive Bayes' theorem, demonstrate how it allows us to update our beliefs with new information, and we'll apply it to a realistic problem.
0:00 Bayesian Methods Introduction
0:42 Deriving Bayes' Theorem - Events and Probabilities
2:04 Conditional Probabilities and Bayes' Theorem
4:18 Updating Beliefs via Bayes' Theorem - Shipping a Product
7:30 Application of Bayes' Theorem - Medical Test Problem
10:11 The Two Key Factors that Influence Updated Beliefs
11:28 Key Bayesian Terminology
12:17 Bayesian Statistics Motivation
#probability #mathematics #datascience #tutorial #bayestheorem #bayesian
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As a quick update, we now have a RiskByNumbers blog (riskbynumbers.org and riskbynumbers.com)! While it is still new, feel free to check it out to learn more about the plans for the platform. You can sign up there to receive the latest updates -- I promise that I will do my best to not spam your inbox! There, I'll soon provide written explainers, which I can do on a more timely basis
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If this is your first video, welcome! I am a professor sharing educational resources around probability, statistics, optimization methods, algorithms, and programming to a broad audience.
Outside of YouTube, you can currently find me in Vancouver, Canada at the University of British Columbia.
Thank you, and I look forward to seeing you in future videos!
Email: [email protected].
LinkedIn: https://www.linkedin.com/in/omar-swei/