2017 Advanced Research Techniques (ART) Forum
June 25-28, 2017
Using Stan to Estimate Hierarchical Bayes Models
Elea McDonnell Feit, Assistant Professor, Drexel University
Kevin Van Horn, Senior Data Science Engineer, Adobe
Stan (http://mc-stan.org/) is a new open-source tool for estimating complex statistical models using MCMC. Similar to JAGS and BUGS, Stan allows you to specify complex models using a modeling language. Once the model is specified, Stan automatically generates a routine to sample from the posterior, allowing the user to focus on the model and posterior estimates, rather than on details of the MCMC routine. In this hands-on tutorial, we will show how to use Stan to fit popular models in marketing including the hierarchical choice models typically used for conjoint, a nested logit model for the no-choice option and data fusion models. Users will be encouraged to run code on their own laptops during the workshop. Stan can be accessed through R, Python or Matlab and several other statistical programming tools; our focus will be on using the RStan interface. Users who are already familiar with Bayesian inference and basic R syntax will get the most out of this tutorial. Instructions for installing Stan and example R code will be provided. Users are encouraged to install Stan before the tutorial.