| Date |
Lesson |
Slides (main) |
Slides (pdf versions) |
Reading |
Lab |
HW |
|
PART I: INTRODUCTION TO BAYESIAN INFERENCE |
|
|
|
|
|
|
WEEK 1 |
|
|
|
|
|
| Fri, Jan 10 |
Course overview and introduction to Bayesian inference |
|
|
|
|
|
|
WEEK 2 |
|
|
|
|
|
| Mon, Jan 13 |
Lab 1: R review |
|
|
|
|
|
|
PART II: ONE PARAMETER MODELS |
|
|
|
|
|
| Wed, Jan 15 |
Probability review and one parameter models |
|
|
|
|
|
| Fri, Jan 17 |
One parameter models cont'd |
|
|
|
|
|
|
WEEK 3 |
|
|
|
|
|
| Mon, Jan 20 |
No lab: Martin Luther King Jr. Day |
|
|
|
|
|
| Wed, Jan 22 |
One parameter models cont'd; Loss functions and Bayes risk |
|
|
|
|
|
|
Drop/add ends |
|
|
|
|
|
| Fri, Jan 24 |
Poisson model (wrap-up); Monte Carlo approximation and sampling |
|
|
|
|
|
|
WEEK 4 |
|
|
|
|
|
|
PART III: MULTIPARAMETER MODELS AND GIBBS SAMPLING |
|
|
|
|
|
| Mon, Jan 27 |
Lab 2: The Beta-Binomial model |
|
|
|
|
|
| Wed, Jan 29 |
Rejection sampling; introduction to the normal model |
|
|
|
|
|
| Fri, Jan 31 |
Normal model cont'd |
|
|
|
|
|
|
WEEK 5 |
|
|
|
|
|
| Mon, Feb 3 |
Lab 3: The Poisson model and posterior predictive checks |
|
|
|
|
|
| Wed, Feb 5 |
Gibbs sampling |
|
|
|
|
|
| Fri, Feb 7 |
Gibbs sampling cont'd |
|
|
|
|
|
|
WEEK 6 |
|
|
|
|
|
| Mon, Feb 10 |
Lab 4: Prior selection and model reparameterization |
|
|
|
|
|
| Wed, Feb 12 |
Quiz I |
|
|
|
|
|
|
The multinomial model |
|
|
|
|
|
| Fri, Feb 14 |
In-class exercise; introduction to multivariate normal |
|
|
|
|
|
|
WEEK 7 |
|
|
|
|
|
| Mon, Feb 17 |
Lab 5: Truncated data |
|
|
|
|
|
|
PART IV: MULTIVARIATE DATA |
|
|
|
|
|
| Wed, Feb 19 |
Multivariate normal model |
|
|
|
|
|
| Fri, Feb 21 |
Multivariate normal model cont'd |
|
|
|
|
|
|
WEEK 8 |
|
|
|
|
|
| Mon, Feb 24 |
Lab 6: Gibbs sampling with block updates |
|
|
|
|
|
| Wed, Feb 26 |
Multivariate normal cont'd; missing data and imputation |
|
|
|
|
|
|
PART V: HIERARCHICAL MODELS |
|
|
|
|
|
| Fri, Feb 28 |
Hierarchical models I |
|
|
|
|
|
|
WEEK 9 |
|
|
|
|
|
| Mon, Mar 2 |
Lab 7: Introduction to Hamiltonian Monte Carlo |
|
|
|
|
|
| Wed, Mar 4 |
Review for Midterm |
|
|
|
|
|
| Fri, Mar 6 |
Midterm exam; Spring break begins 7:00pm |
|
|
|
|
|
|
WEEK 10 |
|
|
|
|
|
| Mon, Mar 9 |
No lab: spring break |
|
|
|
|
|
| Wed, Mar 11 |
No class: spring break |
|
|
|
|
|
| Fri, Mar 13 |
No class: spring break |
|
|
|
|
|
|
WEEK 11 |
|
|
|
|
|
| Mon, Mar 16 |
No lab: extended spring break |
|
|
|
|
|
| Wed, Mar 18 |
No class: extended spring break |
|
|
|
|
|
| Fri, Mar 20 |
No class: extended spring break |
|
|
|
|
|
|
WEEK 12 |
|
|
|
|
|
| Mon, Mar 23 |
Lab 8: Hierarchical modeling |
|
|
|
|
|
|
PART VI: REGRESSION MODELS AND METROPOLIS-HASTINGS |
|
|
|
|
|
| Wed, Mar 25 |
Hierarchical models II |
|
|
|
|
|
| Fri, Mar 27 |
Introduction to regression models |
|
|
|
|
|
|
WEEK 13 |
|
|
|
|
|
| Mon, Mar 30 |
Lab 9: Bayesian (Generalized) Linear Regression Models |
|
|
|
|
|
| Wed, Apr 1 |
Regression models cont'd |
|
|
|
|
|
| Fri, Apr 3 |
Metropolis and Metropolis-Hastings I |
|
|
|
|
|
|
WEEK 14 |
|
|
|
|
|
| Mon, Apr 6 |
Lab 10: Metropolis-Hastings |
|
|
|
|
|
| Wed, Apr 8 |
Metropolis and Metropolis-Hastings II |
|
|
|
|
|
| Fri, Apr 10 |
Metropolis-Hastings; Introduction to finite mixture models |
|
|
|
|
|
|
WEEK 15 |
|
|
|
|
|
| Mon, Apr 13 |
No lab |
|
|
|
|
|
| Wed, Apr 15 |
Finite mixture models cont'd; course wrap-up and brief review session |
|
|
|
|
|
| Fri, Apr 17 |
No class: reading period |
|
|
|
|
|
| Mon, Apr 20 |
No lab: reading period |
|
|
|
|
|
| Wed, Apr 22 |
No class: reading period |
|
|
|
|
|
| Fri, Apr 24 |
No class: reading period |
|
|
|
|
|
| Mon, Apr 27 |
No lab: reading period |
|
|
|
|
|
| Wed, Apr 29 |
No class: reading period |
|
|
|
|
|
| Fri, May 1 |
No class: reading period |
|
|
|
|
|
| Sat, May 2 |
Final exam (online) |
|
|
|
|
|