Bayesian graphical modelling: a case-study in monitoring health outcomes |
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Authors: | David J. Spiegelhalter |
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Affiliation: | Medical Research Council, Biostatistics Unit, Cambridge, UK |
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Abstract: | 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. |
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Keywords: | Cancer incidence Cervical screening Gibbs sampling Hierarchical models Markov chain Monte Carlo methods |
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