The prior model

Bayesian Modeling with RJAGS

Alicia Johnson

Associate Professor, Macalester College

Course goals

Explore foundational, generalizable Bayesian models (eg: Beta-Binomial, Normal-Normal, and Bayesian regression)
Define, compile, and simulate Bayesian models using RJAGS
Conduct Bayesian posterior inference using RJAGS output

Bayesian elections: The prior

Bayesian elections: The prior

Bayesian elections: The prior

Bayesian elections: The data

Bayesian elections: The posterior

Bayesian elections: New data

Bayesian elections: New posterior

Bayesian elections: Newer data

Bayesian elections: Newer posterior

Bayesian thinking

A Bayesian posterior model:

Combines insights from the prior model & observed data
Evolves as new data come in

Building a prior model

$p$ = proportion that support you
$p$ is between 0 and 1
The prior model for $p$ is a Beta distribution with shape parameters 45 and 55

$$p \sim \text{Beta}(45, 55)$$

Tuning the prior

Let's practice!

Bayesian Modeling with RJAGS

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