Short course on Bayesian data analysis and Stan 18-20 July in NYC!

Jonah Gabry, Vince Dorie, and I are giving a 3-day short course in two weeks.

Before class everyone should install R, RStudio and RStan on their computers. (If you already have these, please update to the latest version of R and the latest version of Stan, which is 2.10.) If problems occur please join the stan-users group and post any questions. It’s important that all participants get Stan running and bring their laptops to the course.

Class structure and example topics for the three days:

Monday, July 18: Introduction to Bayes and Stan
Morning:
Intro to Bayes
Intro to Stan
The statistical crisis in science
Afternoon:
Stan by example
Components of a Stan program
Little data: how traditional statistical ideas remain relevant in a big data world

Tuesday, July 19: Computation, Monte Carlo and Applied Modeling
Morning:
Computation with Monte Carlo Methods
Debugging in Stan
Generalizing from sample to population
Afternoon:
Multilevel regression and generalized linear models
Computation and Inference in Stan
Why we don’t (usually) have to worry about multiple comparisons

Wednesday, July 20: Advanced Stan and Big Data
Morning:
Vectors, matrices, and transformations
Mixture models and complex data structures in Stan
Hierarchical modeling and prior information
Afternoon:
Bayesian computation for big data
Advanced Stan programming
Open problems in Bayesian data analysis

Specific topics on Bayesian inference and computation include, but are not limited to:
Bayesian inference and prediction
Naive Bayes, supervised, and unsupervised classification
Overview of Monte Carlo methods
Convergence and effective sample size
Hamiltonian Monte Carlo and the no-U-turn sampler
Continuous and discrete-data regression models
Mixture models
Measurement-error and item-response models

Specific topics on Stan include, but are not limited to:
Reproducible research
Probabilistic programming
Stan syntax and programming
Optimization
Warmup, adaptation, and convergence
Identifiability and problematic posteriors
Handling missing data
Ragged and sparse data structures
Gaussian processes

Again, information on the course is here.

The course is organized by Lander Analytics.

The course is not cheap. Stan is open-source, and we organize these courses to raise money to support the programming required to keep Stan up to date. We hope and believe that the course is more than worth the money you pay for it, but we hope you’ll also feel good, knowing that this money is being used directly to support Stan R&D.

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