8.12 Exercises
For the example in the Covariance functions and kernels section, make sure you understand the relationship between the input data and the generated covariance matrix. Try using other input such as
data = np.random.normal(size=4).Rerun the code generating Figure 8.3 and increase the number of samples obtained from the GP prior to around 200. In the original figure, the number of samples is 2. What is the range of the generated values?
For the generated plot in the previous exercise, compute the standard deviation for the values at each point. Do this in the following form:
Visually, just observing the plots
Directly from the values generated from
pz.MVNormal(.).rvsBy inspecting the covariance matrix (if you have doubts go back to exercise 1)
Did the values you get from these three methods match?
Use test points
np.linspace(np.floor(x.min()), 20, 100)[:,None]and re-runmodel_reg. Plot the results. What did you observe? How is this related to the specification...
