| 1 |
August 25 |
Introduction to Bayesian thinking + {targets} |
1 |
1 |
1 |
|
| 2 |
September 1 |
Starting with Bayes rule, models, and regression |
2–3 |
2 |
2–3 |
|
| 3 |
September 8 |
Regression, priors, and conjugates |
4–5 |
3–4 |
4–5 |
|
| — |
September 15 |
(off) |
— |
— |
— |
— |
| 4 |
September 22 |
DAGs, confounders, and controls |
5–6 |
5–6 |
— |
|
| 5 |
September 29 |
Markov chains and posteriors |
9 |
8 |
6–8 |
|
| 6 |
October 6 |
Overfitting, evaluation, and diagnostics |
7 |
7 |
9–11 |
|
| 7 |
October 13 |
Logistic and Poisson regression |
10–11 |
9–10 |
12–13 |
|
| 8 |
October 20 |
Ordered regression |
12 |
11 |
— |
|
| 9 |
October 27 |
Multilevel models I |
13 |
12 |
15–17 |
|
| 10 |
November 3 |
Multilevel models II |
— |
— |
— |
|
| 11 |
November 10 |
Multilevel models III |
14 |
13–14 |
18–19 |
|
| 12 |
November 17 |
Fancier applications |
14 |
15–16 |
14 |
|
| 13 |
December 1 |
Measurement error and missing data |
15 |
17–18 |
— |
|
| 14 |
December 8 |
Beyond GLMs |
16–17 |
19–20 |
— |
|