Introduction and Purpose In the last series of examples, I focused on Bayesian modeling using the Stan package. This useful package on the surface makes Bayesian analysis a lot easier, but from my point of view the real power (of this and other packages such as JAGS and BUGS) is the ability to specify a model directly from the science and a few statistical ideas.

Purpose In the last few posts, we have built a rather sophisticated linear model using Stan. This model investigates the patterns in flight departure data over the course of a year.

Purpose In the previous post, we took advantage of the rich structure of the Greenville flight data, especially the notions that the number of flights could depend on the weekday and the season of the flights.

Introduction In previous posts on Stan, we examined a dataset of flight departures from GSP international airport. We fit and interpreted a very simple model (simple mean plus random variation).

Purpose The purpose of this post is to introduce you to analyzing Stan output. We started doing this in the last post by printing out the results of the fit object returned by the stan() function.

Introduction and purpose This is a new series on Bayesian analysis using Stan, and, specifically, the R interface to Stan. Even if you don’t want to use the R interface to Stan, much of the actual Stan code may still apply to you, but for now you are on your own getting data into and back out.