Schedule (topic flow)

A 15-week pacing plan — the LMS is authoritative for dates and deadlines

This is a pacing plan, not a calendar. It shows the order and arc of topics. Dates, deadlines, exam windows, and project checkpoints are authoritative in Blackboard (the LMS) and are not set here.

The term moves in five parts, from “what does it mean to reason with uncertainty?” to fitting, checking, and communicating Bayesian models.

Part I — Foundations of Bayesian reasoning (Weeks 1–2)

Week	Theme	The throughline
1	Thinking under uncertainty	Probability as a language for uncertainty; what changes when evidence arrives.
2	Discrete Bayes & diagnostic reasoning	Conditional probability, base rates, false positives, and updating.

Part II — Building Bayesian models and the posterior (Weeks 3–7)

Week	Theme	The throughline
3	Prior, likelihood, posterior	Building a model from assumptions and data; posterior ∝ likelihood × prior.
4	The Beta-Binomial model	Proportions, conjugacy, posterior simulation, credible intervals. (Lab 4)
5	Prior sensitivity & summaries	How conclusions change when assumptions change.
6	Beyond proportions, and posterior prediction	Counts and means, prediction, replicated data, and checking fit.
7	Simulation-first computation	Grid approximation, posterior draws, reproducible Quarto. (Lab 7)

Part III — Synthesis & midterm (Week 8)

Week	Theme	The throughline
8	Midterm synthesis	Pulling together updating, simple models, summaries, and interpretation.

Part IV — Bayesian regression & model checking (Weeks 9–12)

Week	Theme	The throughline
9	Bayesian regression I	Modeling a numerical outcome; priors on regression parameters. (Lab 9)
10	Bayesian regression II	Multiple predictors, binary outcomes, prediction.
11	Model checking & comparison	Posterior predictive checks, predictive performance, model limits.
12	Bayesian & classical in conversation	Credible vs. confidence intervals, posterior probabilities vs. p-values.

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

Week	Theme	The throughline
13	Hierarchical models	Complete pooling, no pooling, partial pooling, and shrinkage. (Lab 13)
14	Decisions & communication	Decision thresholds, uncertainty communication, project work.
15	Course synthesis & the shape of a report	The whole course as one argument, and what a Bayesian report must state.

Holiday weeks, the exam window, and project checkpoints adjust this rhythm; the LMS carries the actual calendar. The final exam is cumulative and scheduled in the registrar’s final-exam block.