Fast Bayesian modelling in Stan using the StataStan program

Robert Grant ()
Additional contact information
Robert Grant: St George's, University of London

United Kingdom Stata Users' Group Meetings 2015 from Stata Users Group

Abstract: Over the last three years, a new package for Bayesian modelling called Stan (after Stanislaw Ulam, co-author of the Metropolis algorithm) has been developing quickly and making an impact on computing for complex Bayesian models. By translating the model into C++ and then compiling that, it can run much faster than BUGS. A particular benefit is for simulation studies, because the model only needs to be compiled once. Furthermore, it includes a much faster and better mixing algorithm (NUTS: the No U-Turn Sampler), especially for correlated parameters that Gibbs samplers like BUGS cope with badly. I present a program StataStan, which sends your data and specifications to Stan, displays results, and can read the chains of samples back into Stata. There are also specific commands to run the commonly used models in the BUGS and Stan user manuals with your own data, avoiding the need to write the Stan model.

Date: 2015-09-16
References: Add references at CitEc
Citations:

Downloads: (external link)
http://repec.org/usug2015/grant_uksug15.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:usug15:15

Access Statistics for this paper

More papers in United Kingdom Stata Users' Group Meetings 2015 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().