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Learn Bayes, MCMC and Stan 2017! With Andrew Gelman, Jonah Gabry & Michael...

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eBay NYC

625 6th Avenue

Floor 3

New York, NY 10011

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Learn Bayesian Data Analysis (BDA) and Markov chain Monte Carlo (MCMC) computation using Stan with Bayes Master Andrew Gelman and Stan developers Jonah Gabry and Michael Betancourt.

This three-day course consists of three main themes: Bayesian inference and computation; the Stan programming language; applied statistics.

Participants will receive a copy of Andrew Gelman's landmark book Bayesian Data Analysis. Proceeds from the class support further development of Stan and the New York Open Statistical Programming Meetup.

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 is 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 (e.g., 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

Topics

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

For more information please contact us.


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eBay NYC

625 6th Avenue

Floor 3

New York, NY 10011

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