Introducing the StataStan interface for fast, complex Bayesian modeling using Stan

Robert L. Grant (), Bob Carpenter, Daniel C. Furr and Andrew Gelman
Additional contact information
Robert L. Grant: BayesCamp
Bob Carpenter: Columbia University
Daniel C. Furr: University of California at Berkeley
Andrew Gelman: Columbia University

Stata Journal, 2017, vol. 17, issue 2, 330-342

Abstract: In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models. Copyright 2017 by StataCorp LP.

Keywords: stan; windowsmonitor; StataStan; Bayesian; bayesmh; interface; shell commands; Stan (search for similar items in EconPapers)
Date: 2017
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