Jonah “ShinyStan” Gabry, Mike “Riemannian NUTS” Betancourt, and I will be giving a three-day short course next month in New York, following the model of our successful courses in 2015 and 2016.

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.) 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:

Day 1: Foundations
Foundations of Bayesian inference
Foundations of Bayesian computation with Markov chain Monte Carlo
Intro to Stan with hands-on exercises
Real-life Stan
Bayesian workflow

Day 2: Linear and Generalized Linear Models
Foundations of Bayesian regression
Fitting GLMs in Stan (logistic regression, Poisson regression)
Diagnosing model misfit using graphical posterior predictive checks
Little data: How traditional statistical ideas remain relevant in a big data world
Generalizing from sample to population (surveys, Xbox example, etc)

Day 3: Hierarchical Models
Foundations of Bayesian hierarchical/multilevel models
Accurately fitting hierarchical models in Stan
Why we don’t (usually) have to worry about multiple comparisons
Hierarchical modeling and prior information

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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