Exercises

1. This exercise is about regularization priors. In the code that generates the data, change order=2 to another value, such as order=5. Then, fit model_p and plot the resulting curve. Repeat this, but now using a prior for beta with sd=100 instead of sd=1 and plot the resulting curve. How are both curves different? Try this out with sd=np.array([10, 0.1, 0.1, 0.1, 0.1]), too.
2. Repeat the previous exercise but increase the amount of data to 500 data points.
3. Fit a cubic model (order 3), compute WAIC and LOO, plot the results, and compare them with the linear and quadratic models.
4. Use pm.sample_posterior_predictive() to rerun the PPC example, but this time, plot the values of y instead of the values of the mean.
5. Read and run the posterior predictive example from PyMC3's documentation at https://pymc-devs.github.io/pymc3/notebooks/posterior_predictive.html. Pay special...