Bayesian graphical modelling: a case-study in monitoring health outcomes

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Bayesian graphical modelling represents the synthesis of several recent developments in applied complex modelling. After describing a moderately challenging real example, we show how graphical models and Markov chain Monte Carlo methods naturally provide a direct path between model specification and the computational means of making inferences on that model. These ideas are illustrated with a range of modelling issues related to our example. An appendix discusses the BUGS software.

Keywords: Cancer incidence; Cervical screening; Gibbs sampling; Hierarchical models; Markov chain Monte Carlo methods

Document Type: Original Article


Affiliations: Medical Research Council, Biostatistics Unit, Cambridge, UK

Publication date: January 1, 1998

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