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.

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 ...

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