Bayesian Analysis using Rstan

 

Lecturers Dr. Benjamin Goodrich, Columbia University, NY
Certificate Confirmation of participation
Target audience

Novice and advanced R users from all professional groups.

Costs
  • CHF 600.- for members of UZH/ETH and associated institutes
  • CHF 800.- for alumni of UZH/ETH, members of other universities, the public sector and non-profit organizations
  • CHF 1200.- for companies
Persons without current employment can register for the UZH/ETH fee upon request.
Course language English
Course description

This two-day course will introduce participants to Bayesian analysis using R interfaces to the Stan language and algorithms. No previous experience with Stan is necessary, because the Stan language is similar to R. By the end of the course, participants will be able to write simple models themselves in the Stan language and draw from the posterior distribution of the parameters, as well as accessing the posterior draws generated by other R packages that come with more complicated Stan programs.

More specifically, the first day of the course will cover:

  • Principles of Bayesian analysis and how it differs from other forms of analysis
  • The syntax of the Stan language that is exposed via the rstan R package
  • Estimating generalized linear (mixed) models using the Stan programs in the rstanarm R package
  • Visualizing, analyzing, and comparing the results using the shinystan, bayesplot, and loo R packages

The second day of the course will cover:

  • Estimating Bayesian models using the Stan programs generated by the brms R package
  • Writing your own Stan programs and validating that they are correct
  • Including your own Stan programs in new R packages using the rstantools R package

Prerequisites

Dates March 19-20, 2020
[canceled]
  After registering you will receive a short automatic confirmation by email. If you received this email you are successfully and bindingly registered for the course. For administrative reasons the written invoice won't be sent out until about two weeks before the course.