Stan has become a popular alternative to other Bayesian modeling software such as OpenBUGS and JAGS. In this workshop, we will introduce Stan for Bayesian modeling. The workshop will include an overview of Bayesian modeling, structure of Stan modeling language, tips and tricks to improve convergence and efficiency, advice on how to handle common convergence issues, and lab exercises so participants can get hands-on experience. Lab exercises will include linear and non-linear models and mixed effects models. At the end of the workshop, participants will have a basic understanding of how to use Stan for Bayesian modeling. Previous experience with R is beneficial but not required. You are encouraged to bring your laptop so that you can continue working in Stan after the workshop.
The workshop demonstration and examples will use RStan, an R interface to Stan. Participants are asked to install R, RStudio, and RStan before the workshop. All software is open source (FREE!).
RStan: http://mc-stan.org/users/interfaces/rstan.html
Contact the instructor if you have questions about installing the necessary software, [email protected]