Summarizing the posterior

Generally, the first task we will perform after sampling from the posterior is check what the results look like. The plot_trace function from ArviZ is ideally suited to this task:

az.plot_trace(trace)

Figure 2.1

By using az.plot_trace, we get two subplots for each unobserved variable. The only unobserved variable in our model is . Notice that y is an observed variable representing the data; we do not need to sample that because we already know those values. Thus, in Figure 2.1, we have two subplots. On the left, we have a Kernel Density Estimation (KDE) plot; this is like the smooth version of the histogram. On the right, we get the individual sampled values at each step during the sampling. From the trace plot, we can visually get the plausible values from the posterior. You should compare this result using PyMC3 with those from the previous chapter...