Bayesian Inference for the Normal Model
The normal distribution has two parameters, but we focus on the one-parameter setting in this lecture. We also introduce the posterior predictive check as a way to assess model fit, and briefly discuss the issue with improper prior distributions.
Monte Carlo Sampling
This lecture discusses Monte Carlo approximations of the posterior distribution and summaries from it. While this might not seem entirely useful now, this underlies some of the key computational methods used for Bayesian inference that we will discuss further.
Bayesian Inference for the Poisson Model
This lecture discusses Bayesian inference for the Poisson model, including conjugate prior specification, a different way to specify a "non-informative" prior, and relevant posterior summaries.