This week, Ellis Hughes and I shift our initial intro to Bayes towards solving an actual problem using the beta distribution as a conjugate prior for a binomial likelihood. We discuss what a conjugate prior is, we cover updating your prior knowledge in line, as new data becomes available, and show how the posterior distribution, produced by our Bayesian approach, works to change our beliefs about a specific outcome.
Aside from this episode, we also discussed this approach in TidyX Episode 11, where we used the a beta prior to update our knowledge about the winning percentage of professional beach volleyball teams across a season. That episode also discussed how to use the method of moments (sometimes referred to as moment matching) to select the alpha and beta parameters for the beta distribution, if you don’t have a good sense of what they should be in order to establish your prior.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
To access the screen cast and code for Episode 11, CLICK HERE.