Bayesian techniques provide a rational way of combining probabilistic information, enabling sophisticated analyses of scientific datasets and principled models of action under uncertainty. This course introduces Bayesian techniques from the ground up.
Week | Date | Topic | Reading | Materials | Assignments |
---|---|---|---|---|---|
1 | Mar 31 | Introduction, basic probability theory | DBDA 1 | ||
2 | Apr 5, 7 | R, more probability theory, Bayes' rule | DBDA 4–5 | hw1 out Apr 8 | |
3 | Apr 12, 14 | More R, models for binary outcomes, exact inference | DBDA 6 | hw1 due Apr 15 | |
4 | Apr 19, 21 | Markov chain Monte Carlo (MCMC), JAGS | DBDA 7–8 | hw2 out Apr 22 | |
5 | Apr 26, 28 | Hierarchical models, more MCMC | DBDA 8–10 | hw2 due Apr 29 | |
6 | May 3, 5 | Gaussians, linear models, mixture models | DBDA 15–16 | hw3 out May 6; Project proposal due May 6 | |
7 | May 10, 12 | Linear regression with continuous predictors | DBDA 17–18 | hw3 due May 13 (NOW May 17) | |
8 | May 17, 19 | Mixed-effects regression | DBDA 19–21 | regression code | hw4 out May 20 |
9 | May 24, 26 | Logistic regression; Bayesian vs. frequentist inference | DBDA 11 | hw4 due May 27 NOW MAY 30 | |
10 | May 31 | Computational psycholinguistics | |||
Jun 7 | Final project papers due 5pm |