Notes

The weekly instructional spine

These are the course’s weekly notes — the place to learn or re-learn the ideas if you missed class. Each week develops one stretch of the throughline, works concrete examples, names the mistakes that are easy to make, and maps that week’s reading. They are public, ungraded material; graded work lives in the LMS.

How to read a week

Every week note follows the same shape so you always know where to look:

Where we are — what last week established and what this week adds.
Concept development — the ideas, built rather than asserted.
Worked examples — concrete (often recurring) cases worked end to end.
A common mistake — the trap to avoid, and how to tell you have fallen into it.
Practice — ungraded checks for understanding (no answer keys here).
Reading guide — the Bayes Rules! (and other open) sections that reinforce the week.
Looking ahead — the bridge to next week.

A recurring example — estimating an unknown proportion (a success rate) — runs through much of the term so you can watch one problem grow from a discrete updating table into a full Bayesian model.

The five parts

Part I — Foundations (Wk 1–2). What it means to reason with uncertainty, and discrete Bayes’ rule through diagnostic testing.

Part II — Building Bayesian models (Wk 3–7). From prior/likelihood/posterior to the Beta-Binomial model, prior sensitivity, posterior prediction, and simulation.

Part III — Synthesis & midterm (Wk 8).

Midterm synthesis

Part IV — Regression & model checking (Wk 9–12).

Part V — Hierarchy, decisions & project (Wk 13–15).