Introduction to Statistics and Data Analysis - Bayesian Updates and Conjugate Priors

This video describes how to update Bayesian models with new information, and the importance of conjugate priors. As the posterior becomes the prior for the next update, it is helpful if the likelihood times the prior stay in the same distribution. This is the basic idea of conjugate priors, so that the likelihood and prior become "conjugate". This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 02:42 Proportionality of Bayesian Prior and Posterior 03:30 Why Conjugate Priors 05:32 Example: Binomial and Beta Distributions 08:07 Conjugate Prior Definition (Informal) 09:24 Other Conjugate Priors 10:49 Updating Beta 12:07 Code Demo: Coin Flips 17:01 Outro