Fitting Bayesian item response models in Stata and Stan
Robert L. Grant
BayesCamp
Croydon, UK
[email protected]
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Daniel C. Furr
University of California at Berkeley
Berkeley, CA
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Bob Carpenter
Columbia University
New York, NY
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Andrew Gelman
Columbia University
New York, NY
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Abstract. Stata users have access to two easy-to-use implementations of Bayesian
inference: Stata’s native bayesmh command and StataStan, which calls the
general Bayesian engine, Stan. We compare these implementations on two
important models for education research: the Rasch model and the hierarchical
Rasch model. StataStan fits a more general range of models than can be fit by
bayesmh and uses a superior sampling algorithm, that is, Hamiltonian
Monte Carlo using the no-U-turn sampler. Furthermore, StataStan can run in
parallel on multiple CPU cores, regardless of the flavor of Stata. Given these
advantages and given that Stan is open source and can be run directly from
Stata do-files, we recommend that Stata users interested in Bayesian methods
consider using StataStan.
View all articles by these authors:
Robert L. Grant, Daniel C. Furr, Bob Carpenter, Andrew Gelman
View all articles with these keywords:
stan, windowsmonitor, StataStan, bayesmh, Bayesian
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