Bayesian graphical modelling: a case‐study in monitoring health outcomes | Zendy

Spiegelhalter David J. | Zendy

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

Author(s) -

Spiegelhalter David J.

Publication year - 1998

Publication title -

journal of the royal statistical society: series c (applied statistics)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.205

H-Index - 72

eISSN - 1467-9876

pISSN - 0035-9254

DOI - 10.1111/1467-9876.00101

Subject(s) - graphical model , computer science , markov chain monte carlo , bayesian probability , range (aeronautics) , software , path (computing) , variable order bayesian network , monte carlo method , data mining , bayesian inference , machine learning , artificial intelligence , programming language , mathematics , statistics , engineering , aerospace engineering

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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