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Performing Bayesian analysis in Stata using WinBUGS


  • 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)


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.

Suggested Citation

  • Tom Palmer & John Thompson & Santiago Moreno, 2007. "Performing Bayesian analysis in Stata using WinBUGS," United Kingdom Stata Users' Group Meetings 2007 08, Stata Users Group.
  • Handle: RePEc:boc:usug07:08

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    References listed on IDEAS

    1. Roger Newson, 2006. "Confidence intervals for rank statistics: Percentile slopes, differences, and ratios," Stata Journal, StataCorp LP, vol. 6(4), pages 497-520, December.
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