GLM poisson

File: https://github.com/pymc-devs/pymc-examples/blob/main/examples/generalized_linear_models/GLM-poisson-regression.ipynb
Reviewers:

The sections below may still be pending. If so, the issue is still available, it simply doesn't
have specific guidance yet. Please refer to this overview of updates

Known changes needed

Changes listed in this section should all be done at some point in order to get this
notebook to a "Best Practices" state. However, these are probably not enough!
Make sure to thoroughly review the notebook and search for other updates.

General updates

• Use numpy generator
• ⚠️ code cells 15 and 19 are plain wrong, we are doing np.exp(np.mean()) instead of np.mean(np.exp()).

ArviZ related

• code cell 15 (again) is computing the whole summary dataframe, when only a subset of the columns are needed. We should either use kind="stats" or customize summary, examples of both at: https://arviz-devs.github.io/arviz/api/generated/arviz.summary.html

Notes

Exotic dependencies

None

Computing requirements

Models sample in less than a minute

Open examples/generalized_linear_models/GLM-poisson-regression.ipynb and review the listed updates, starting with code cells 15 and 19. Replace the random-number usage with a NumPy generator, correct the exponential mean calculation, and limit the ArviZ summary as described; rerun the notebook and confirm the models still sample in under a minute.