Joint Meetup with Ann Arbor ASA: Spatial Models in Stan


The Ann Arbor R Users' Group and the Ann Arbor Chapter of the American Statistical Association are excited to host Mitzi Morris, a core developer for Stan. Mitzi has offered to give a version of a talk she gave at StanCon on using Stan for areal data, taken from her case study on ICAR models.

Stan is freedom-respecting, open-source software (new BSD core, some interfaces GPLv3) for facilitating statistical inference at the frontiers of applied statistics. Stan provides full Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo (HMC), approximate Bayesian inference using automatic differentiation variational inference (ADVI), and penalized maximum likelihood estimation (MLE) using L-BFGS optimization. Stan has interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line.