As we discussed at the beginning of this chapter, linear models are very useful statistical tools. Extensions such as the ones we saw in this chapter make them even more general tools. For that reason, PyMC3 includes a module to simplify the creation of linear models: the Generalized Liner Model (GLM) module. For example, a simple linear regression will be as follows:
with pm.Model() as model:
glm.glm('y ~ x', data)
trace = sample(2000)
The second line of the preceding code takes care of adding priors for the intercept and for the slope. By default, the intercept is assigned a flat prior, and the slopes an 
prior. Note that the maximum a posteriori (MAP) of the default model will be essentially equivalent to the one obtained using the ordinary least squared method. These is totally fine as a default linear regression; you can change it using...
