Statistics and Computing

, Volume 10, Issue 4, pp 325–337

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

  • David J. Lunn
  • Andrew Thomas
  • Nicky Best
  • David Spiegelhalter
Article

DOI: 10.1023/A:1008929526011

Cite this article as:
Lunn, D.J., Thomas, A., Best, N. et al. Statistics and Computing (2000) 10: 325. doi:10.1023/A:1008929526011

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.

WinBUGSBUGSMarkov chain Monte Carlodirected acyclic graphsobject-orientationtype extensionrun-time linking

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • David J. Lunn
    • 1
  • Andrew Thomas
    • 2
  • Nicky Best
    • 3
  • David Spiegelhalter
    • 4
  1. 1.Department of Epidemiology and Public HealthImperial College School of MedicineLondonUK
  2. 2.Department of Epidemiology and Public HealthImperial College School of MedicineLondonUK
  3. 3.Department of Epidemiology and Public HealthImperial College School of MedicineLondonUK
  4. 4.Medical Research Council Biostatistics UnitInstitute of Public HealthCambridgeUK