Learning Bayesian Models with R

Chapter 5. Bayesian Regression Models

In the previous chapter, we covered the theory of Bayesian linear regression in some detail. In this chapter, we will take a sample problem and illustrate how it can be applied to practical situations. For this purpose, we will use the generalized linear model (GLM) packages in R. Firstly, we will give a brief introduction to the concept of GLM to the readers.

Generalized linear regression

Recall that in linear regression, we assume the following functional form between the dependent variable Y and independent variable X:

Here, is a set of basis functions and is the parameter vector. Usually, it is assumed that ...