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Performing Bayesian analysis in Stata using WinBUGS

Listed author(s):
  • Tom Palmer


    (Department of Health Sciences, University of Leicester)

  • John Thompson

    (Department of Health Sciences, University of Leicester)

  • Santiago Moreno

    (Department of Health Sciences, University of Leicester)

Registered author(s):

    WinBUGS is a program for Bayesian model fitting by Gibbs sampling. WinBUGS has limited facilities for data handling, whereas Stata has excellent data handling but no routines for Bayesian analysis; therefore, much can be gained by running Stata and WinBUGS together. This talk explains the use of the winbugsfromstata package, described in Thompson et al. (2006), a set of programs that enable data to be processed in Stata and then passed to WinBUGS for model fitting. Finally, the results can be read back into Stata for further processing. Examples will be chosen to illustrate the range of models that can be fitted within WinBUGS and where possible the results will be compared with frequentist analyses in Stata. Issues to consider when fitting models under Markov Chain Monte Carlo methods will be discussed including assessment of convergence, length of burn-in and the form and impact of prior distributions. J. Thompson, T. Palmer, and S. Moreno, 2006, Bayesian analysis in Stata with WinBUGS, The Stata Journal, 6(4), p530–549.

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    Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2007 with number 08.

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    Date of creation: 14 Sep 2007
    Handle: RePEc:boc:usug07:08
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