/class_20170809

Primary LanguageHTML

Course Description

This repo contains all of the course materials for the 1-day Stan course on 08/09/2017.

Instructor

I'm Rob Trangucci, and I've been a Stan developer for the past 3 years. I've been funded by YouGov to do general research and software development for the Stan library. I focus on hierarchical modeling, Gaussian processes, and writing the matrix functions for the Stan math library.

Email: robert.trangucci@gmail.com GitHub: https://github.com/rtrangucci

Prerequisite software

Necessary software

Come to class with R installed on your machines and the following packages installed:

  • rstan
  • rstanarm
  • bayesplot
  • dplyr
  • ggplot2

You can also just run this script to install the packages that you don't have.

if (!require("rstanarm")) install.packages("rstanarm")
if (!require("bayesplot")) install.packages("bayesplot")

These packages will install ggplot2 and dplyr if they're not already installed. As for installing rstan, Mac and Linux users with recent OS versions and Xcode installed (for Mac users) should have no problem using install.packages("rstan"). If you do run into problems, follow these instructions.

Windows users should follow these instructions

Optional software

  • devtools
  • RStudio
    • This will make viewing the Rmd files much easier
  • shinystan

Class format

I'll walk through a presentation from 9:30 to 10:30 that'll introduce everyone to Stan, and some bedrock principles needed to understand how Stan works. Then we'll walk through four RMarkdown documents, which you'll have access to, and we'll work through writing and fitting models to both real and generated data. I'll highlight key takeaways for each example and how they apply to running models over real data. When the class is over you'll have Stan and RStanArm code that'll generalize across datasets, along with all the data prep code and commentary in the RMarkdown docs.

Schedule

Time Topic
09:00 AM to 09:30 AM Intro and coffee
09:30 AM to 11:30 AM Hierarchical models in Stan
11:30 AM to 12:00 PM Coffee and questions
12:00 PM to 01:30 PM Logistic regression, postrat in Stan and rstanarm
01:30 PM to 02:00 PM Lunch
02:30 PM to 04:30 PM Multinomial logit in Stan
04:30 PM to 05:30 PM Review and questions

Knitted examples

Hierarchical models in stan

  • Hierarchical models in Stan

    • One-way normal model

      • How to write a Stan model
      • What Stan does under the hood
      • Sampler output and diagnostics
      • What to do about divergences? (Non-centered parameterization)
      • How to modify the target density manually for speed (only if there is time)

    • Linear regression with group-varying intercepts and coefficients

      • Priors for group-varying intercepts and coefficients
      • Priors for covariance matrices
      • LKJ prior for correlation matrices

Logistic regression, poststrat in Stan

Logistic regression, poststrat in RStanArm

Hierarchical multinomial logistic regression

  • Hierarchical multinomial logit
    • Model description
    • Fake data generation for model checking
    • Equivalent models
    • Tricks for speed
    • Priors for hierarchical variance parameters
    • Fit to survey data

Things to remember when optimizing a model

  • Vectorize when possible
  • Statistical efficiency and good model fit are the best antidotes to a slow model (reducing treedepth for each iteration will reduce the time it takes to fit a model by an order of magnitude)
  • Use native Stan functions rather than writing your own in the Stan language
    • Most of Stan's functions have been optimized a lot (see linear regression example)
  • Can you use sampling independence to reduce the size of your dataset?
  • The only expressions that matter are those that are in the kernel of the posterior. Do not worry about constants!
  • Use sufficient statistics to reduce the size of the expression tree whenever possible (examples are IID normal observations at any level linear models)

Useful links

Literature folder table of contents