# Short course on Bayesian data analysis and Stan 19-21 July in NYC!

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Bob Carpenter, Daniel Lee, 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 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:

Sunday, July 19: 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

Monday, July 20: 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

Tuesday, July 21: 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.

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