Skip to main content
Log in

WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility

Statistics and Computing Aims and scope Submit manuscript

Abstract

WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an object-oriented representation of the model. The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design. We also discuss how the framework may be extended. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Ayanian J.Z., Landrum M.B., Normand S.-L.T., Guadagnoli E., and McNeil B.J. 1998. Rating the appropriateness of coronary angiography-Do practicing physicians agree with an expert panel and with each other?. New England Journal of Medicine 338: 1896–1904.

    Google Scholar 

  • Beal S.L. and Sheiner L.B. 1992. NONMEM User's Guide, parts I-VII. NONMEM Project Group, San Francisco.

    Google Scholar 

  • Besag J., Green P., Higdon D., and Mengersen K. 1995. Bayesian computation and stochastic systems. Statistical Science 10: 3–66.

    Google Scholar 

  • Cornell G. and Horstmann C.S. 1997. Core Java, 2nd Edition. Prentice Hall, New Jersey.

    Google Scholar 

  • Gelfand A.E. and Smith A.F.M. 1990. Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85: 398–409.

    Google Scholar 

  • Gelman A., Carlin J.B., Stern H.S., and Rubin D.B. 1995. Bayesian Data Analysis. Chapman and Hall, ondon.

    Google Scholar 

  • Geman S. and Geman D. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721–741.

    Google Scholar 

  • Gilks W. 1992. Derivative-free adaptive rejection sampling for Gibbs sampling. In: Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. (Eds.), Bayesian Statistics 4. Oxford University Press, xford, pp. 641–665.

    Google Scholar 

  • GilksW.R., Richardson S., and Spiegelhalter D.J. 1996. Markov Chain Monte Carlo in Practice. Chapman and Hall, ondon.

    Google Scholar 

  • Gilks W.R. and Wild P. 1992. Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41: 337–348.

    Google Scholar 

  • Hastings W.K. 1970. Monte Carlo sampling-based methods using Markov chains and their applications. Biometrika 57: 97–109.

    Google Scholar 

  • Lauritzen S.L., Dawid A.P., Larsen B.N., and Leimer H.G. 1990. Independence properties of directed Markov fields. Networks 20: 491–505.

    Google Scholar 

  • Lunn D.J. and Aarons L. 1998. The pharmacokinetics of saquinavir: A Markov chain Monte Carlo population analysis. Journal of Pharmacokinetics and Biopharmaceutics 26: 47–74.

    Google Scholar 

  • Lunn D.J., Wakefield J., Thomas A., Best N., and Spiegelhalter D. 1998. PK Bugs User Guide. Dept. Epidemiology and Public Health, Imperial College School of Medicine, London.

    Google Scholar 

  • Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. 1953. Equations of state calculations by fast computing machines. Journal of Chemical Physics 21: 1087–1091.

    Google Scholar 

  • Meyer B. 1997. Object Oriented Software Construction, 2nd Edition. Prentice Hall, New Jersey.

    Google Scholar 

  • Neal R.M. 1997. Markov chain Monte Carlo methods based on 'slicing' the density function. Technical Report 9722, Dept. of Statistics, University of Toronto.

  • Pfister C. 1997. Component Software: A Case Study Using Black Box Components. Oberon microsystems, Inc., Zurich.

    Google Scholar 

  • Pitt M.K. and Shephard N. 1999. Time-varying covariances: A factor stochastic volatility approach. In Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. (Eds.), Bayesian Statistics 6. Oxford University Press, Oxford, pp. 547–570.

    Google Scholar 

  • Reiser M. and Wirth N. 1992. Programming in Oberon: Steps Beyond Pascal and Modula. ACM Press, New York.

    Google Scholar 

  • Ripley B.D. 1987. Stochastic Simulation. Wiley, New York.

    Google Scholar 

  • Spiegelhalter D.J. 1998. Bayesian graphical modelling: A case-study in monitoring health outcomes. Applied Statistics 47: 115–133.

    Google Scholar 

  • Spiegelhalter D.J., BestN.G., GilksW.R., and Inskip H. 1996a. Hepatitis B: A case study in MCMC methods. In Gilks W.R., Richardson S., and Spiegelhalter D.J. (Eds.), Markov Chain Monte Carlo in Practice. Chapman and Hall, London, pp. 21–43.

    Google Scholar 

  • Spiegelhalter D.J., Dawid A.P., Lauritzen S.L., and Cowell R.G. 1993. Bayesian analysis in expert systems (with discussion). Statistical Science 8: 219–283.

    Google Scholar 

  • Spiegelhalter D.J., Thomas A., and Best N.G. 1996. Computation on Bayesian graphical models. In Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. (Eds.), Bayesian Statistics 5. Oxford University Press, Oxford, pp. 407–425.

    Google Scholar 

  • Spiegelhalter D., Thomas A., Best N., and Gilks W. 1996b. BUGS 0.5: Bayesian inference Using Gibbs Sampling-Manual (version ii). Medical Research Council Biostatistics Unit, Cambridge.

    Google Scholar 

  • Szyperski C. 1995. Component-oriented programming: A refined variation of object-oriented programming. The Oberon Tribune 1: 1–5.

    Google Scholar 

  • Wakefield J. and Morris S. 1999. Spatial dependence and errorsin-variables in environmental epidemiology. In Bernardo J.M., Berger J.O., Dawid A.P., and Smith A.F.M. (Eds.), Bayesian Statistics 6. Oxford University Press, Oxford, pp. 657–684.

    Google Scholar 

  • Whittaker J. 1990. Graphical Models in Applied Multivariate Analysis. Wiley, Chichester.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lunn, D.J., Thomas, A., Best, N. et al. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325–337 (2000). https://doi.org/10.1023/A:1008929526011

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008929526011

Navigation