Chapter 3 The Beta-Binomial Bayesian Model

Learning objectives:

• We will learn how to interpret and tune a continuous Beta prior model to reflect your prior information about \(\pi\)

• We will learn how to interpret and communicate features of prior and posterior models using properties such as mean, mode, and variance

• Construct the fundamental Beta-Binomial model for proportion \(\pi\)

To prepare for this chapter, note that we’ll be using three Greek letters throughout our analysis: \(\pi\) = pi, \(\alpha\) = alpha, and \(\beta\) = beta. Further load the packages below.

library(bayesrules)
library(tidyverse)