A set of practice problems for the midterm is available here. They will be augmented with problems on the Bayesian material once we are done with that section.
All readings are optional and might not be at the appropriate level for everyone’s background.
Lecture 1: Overview + fitting by eye
[R code]
[Lecture 2]: Building generative models
[Reading: Bayesian Workflow, Chapter 25]
[Lecture 3]: Generative model examples + Lab
[ipynb with lecture examples, R with lecture examples] [ipynb with lab, Rmd with lab]
[Lecture 4]: Simulated method of moments I
[ipynb with lecture examples, Rmd with lecture examples] [Reading: Shalizi, Chapter 24]
[Lecture 5]: Simulated method of moments II: Identifiability
[ipynb with lecture examples, Rmd with lecture examples] [ipynb with lab, Rmd with lab]
[Lecture 6]: Some variations on the theme
[ipynb with lecture examples, Rmd with lecture examples]
[Reading: Indirect inference, Inference with MMD]
[Lecture 7]: Finish SMM + Parametric bootstrap
[ipynb with lecture examples, Rmd with lecture examples]
[Reading: CASI Section 10.4 on the parametric bootstrap, Python interface to SMM, More about weighting matrices for SMM]
Lecture 8: Parametric bootstrap lab
[Lab materials]
[Lecture 9]: Model checking
[ipynb with lecture examples, Rmd with lecture examples]
[Reading: ROS section 11.4-11.5 (pdf at the link)]
[Lecture 10]: Model checking lab + conditional simulation/rejection sampling
[ipynb with lecture examples, Rmd with lecture examples]
[Lab materials]
[Lecture 11]: Intro to Bayesian inference + rejection sampling
[ipynb with lecture examples, Rmd with lecture examples]
[Lecture 12]: Approximate Bayesian Computation
[ipynb with lecture examples, Rmd with lecture examples]
[Reading: Sisson, Fan, Beaumont, Overview of Approximate Bayesian Computation (This is the open version of a chapter in the Handbook of Approximate Bayesian Computation)]
Lecture 13: ABC lab
[Lab materials]
[Lecture 14]: Visualizing independence/conditional independence
[ipynb with lecture examples, Rmd with lecture examples], [Penguin data]
[Reading: Cook and Laa, Interactively Exploring High-Dimensional Data and Models in R, Chapter 1. Projections and tours.]
[Lecture 15]: Posterior predictive checks, identifiability, ABC vs. SMM comparisons
[ipynb with lecture examples, Rmd with lecture examples]
[Reading: Wickham, Cook, Hofman, Buja. Graphical inference for infovis]