The Center for Social Statistics Presents:
Instructor: Daniel Lee, Columbia University
Abstract:
Stan is an open-source, Bayesian inference tool with interfaces in R, Python, Matlab, Julia, Stata, and the command line. Users write statistical models in a high-level statistical language. The default Bayesian inference algorithm is the no-U-turn sampler (NUTS), an auto-tuned version of Hamiltonian Monte Carlo. Stan was developed to address the speed and scalability issues of existing Bayesian inference tools. The goal of the workshop is the practical application of Stan to different models starting with ordinary linear regression and ending with more complex models such as generalized linear mixed and hierarchical models. Experience in Bayesian statistical modeling is recommended, but not required. Workshop participants should bring their own laptop installed with RStan (or some other Stan interface). See http://mc-stan.org/ for instructions on how to install it.
The workshop is open, but UCLA community members are especially welcomed. To enroll for the workshop, please go to: http://goo.gl/forms/CYHSj8Vkcw
The workshop is co-sponsored by the UCLA Department of Statistics, the UCLA Department of Political Science, and the Department of Biostatistics at UCLA.
