Applied Bayesian Data Analysis for Research / Spring 2023
Updates
- 01/08 -- New Lecture is up: Lecture 5 -- How are Bayesian models fit? Part 2 [slides]
- 01/08 -- New Lecture is up: Lecture 4 -- How are Bayesian models fit? Part 1 [slides]
- 01/08 -- New Lecture is up: Lecture 3 -- Priors, multivariate models, regression [slides]
- 01/08 -- New Lecture is up: Lecture 2 -- Bayesian analysis introduction – single parameter models [slides]
- 01/08 -- New Lecture is up: Lecture 1 - Course Introduction [slides]
- 01/18 -- New Assignment released: [Paper presentation and replication]
- 01/18 -- New Assignment released: [Homework #1]
Course Description
Bayesian modeling and data analysis is a powerful tool for computational research. It consists of writing a probability model and then fitting it with observed data, while handling uncertainty. The model can be flexible, encompassing hierarchy, spatio-temporal dynamics, graphs, and high-dimensionality. This course is a graduate, hands-on introduction to Bayesian analysis in Stan and/or Pyro. The focus will be on writing and fitting models in practice for computational research, including the applied Bayesian statistics workflow: model building, checking, and evaluation. The course will also discuss research papers that use such methods.
Important links
- Course website
- Syllabus
- Github Repo Contains hw assignment and code used in class.
- Canvas
- Paper list
- Paper presentation and final project presentation signup
- Slack – Primary communication tool; see syllabus for link
See the syllabus for details. The class will be hybrid remote, with students split across Ithaca and NYC.
Course topics
- Introduction and overview of Bayesian inference (~1 weeks)
- Developing and debugging basic models in Stan/Pyro (~2 weeks)
- Single parameter models, multivariate models, Bayesian regression, Multinomial choice models, Hierarchical models
- Basics of sampling algorithms
- Bayesian workflow (~1-2 weeks)
- Model checking (posterior predictive checks, diagnosing identifiability issues, etc)
- Evaluating, comparing models
- Research paper case studies (~4-5 weeks)
- Each lecture we will discuss 1 paper. There will be student presenters for the paper (via slides), and then we will replicate in code the model + fitting from the paper.
- Miscellaneous topics (~1-2 weeks)
- Some guest lectures
- Advanced models – gaussian processes/dynamic models, HMMs and Spatial models, conditional random fields, nonlinear and nonparametric models
- Sampling algorithms (Gibbs, Metropolis Hastings, HMC, Variational Inference)
- Computation concerns (speeding up models, GPU usage, etc)
- Student project presentations (1-2 weeks)
