6.11 Summary

In this chapter, we present the core univariate regression models and demonstrate how to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) methods. Specifically, we cover a range of algorithms: Gibbs sampling, Metropolis-Hastings, nested Metropolis-Hastings, and Metropolis-Hastings-within-Gibbs. These algorithms form the foundation for performing Bayesian inference in more complex settings using cross-sectional datasets.