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

Author

Listed:
  • Robert L. Grant

    (BayesCamp)

  • Bob Carpenter

    (Columbia University)

  • Daniel C. Furr

    (University of California at Berkeley)

  • Andrew Gelman

    (Columbia University)

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.

Suggested Citation

  • Robert L. Grant & Bob Carpenter & Daniel C. Furr & Andrew Gelman, 2017. "Introducing the StataStan interface for fast, complex Bayesian modeling using Stan," Stata Journal, StataCorp LP, vol. 17(2), pages 330-342, June.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:2:p:330-342
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