APSTA-GE 2123: Bayesian Inference
This is the home of the Spring 2024 Bayesian Inference class at NYU Steinhardt. If you are enrolled in the course, the assignment will be posted on Brightspace.
This is the current version of the syllabus. Please check the date, as it is subject to change.
Lectures
The following is a preliminary lecture plan. We will add the links after each lecture.
- Lecture 01: Introduction and Bayesian Workflow
- Lecture 02: Conjugate Models and Beta-Binomial
- Lecture 03: More Conjugate Models and Introduction to Posterior Sampling
- Lecture 04: MCMC, Posterior Inference, and Prediction
- Lecture 05: Linear Regression and Model Evaluation
- Lecture 06: Expanding the Linear Model and Modeling Counts
- Lecture 07: Logistic regression and introduction to hierarchical models
The final project is due at the end of the semester, and the presentations will take place during finals week. Use these guidelines when working on your proposal and the final report.
Background Resources
The following resources may be helpful to those who need a refresher on the prerequisites.
R for Data Science 2e, Wickham et al.
Calculus Made Easy, Thompson
Calculus, Strang et al.
YouTube: Essence of Calculus, Sanderson
Intoduction to Probability, Blitzstein et al.
Introduction to Probability Cheatsheet v2, Chen et al.
Additional Bayesian Resources
Statistical Rethinking, McElreath
Bayesian Data Analysis, Gelman et al.
Stan User’s Guide, Stan Development Team