4.4 Counting bikes

How can we change model_lb to better accommodate the bike data? There are two things to note: the number of rented bikes is discrete and it is bounded at 0. This is usually known as count data, which is data that is the result of counting something. Count data is sometimes modeled using a continuous distribution like a Normal, especially when the number of counts is large. But it is often a good idea to use a discrete distribution. Two common choices are the Poisson and NegativeBinomial distributions. The main difference is that for Poisson, the mean and the variance are the same, but if this is not true or even approximately true, then NegativeBinomial may be a better choice as it allows the mean and variance to be different. When in doubt, you can fit both Poisson and NegativeBinomial and see which one provides a better model. We are going to do that in Chapter 5. But for now, we are going to use NegativeBinomial.

Code 4.5

with pm.Model() as model_neg: ...