In this module, we will see how hierarchical models make it easy to deal with categorical data, especially when these data are nested. We will see how they automatically identify the right amount of pooling between data to provide a balance between the complete and no pooling approaches.

In this module, we will extend the Bayesian linear regression to be able to deal with binary (categorical) and count data. We will see the Bernoulli likelihood for the Bayesian logistic regression and how we can extend it to more than two categories through the categorical likelihood. Finally, we will see the Bayesian Poisson regression (and other options) for count data.

In this module, we will see the basic notions behind model selection and the philosophical and practical differences between frequentists and Bayesians on the topic. We will understand the difference between the posterior distribution of the model parameters and the posterior predictive distributions. The latter will lead us to the ideas of posterior predictive checks and model coverage.