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Bayesian Diagnostics for Chain Event Graphs
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AI
DOI：10.1214/24-BA1447.png)
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Abstract
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Chain event graphs have been established as a practical Bayesian graphical tool. While bespoke diagnostics have been developed for Bayesian Networks, they have not yet been defined for the statistical class of Chain Event Graph models. Mirroring the methodology of prequential diagnostics for Bayesian Networks, in this paper we design a number of new Bayesian diagnostics for this new class. These can be used to check whether a selected model - presumably the best within the class - captures most of the salient features of the observed process. These are designed to check the continued validity of a selected model as data about a population is collected. A previous study of health care outcomes illustrates the efficacy of these diagnostics. A second example on breast cancer recurrence offers a more expressive example.
Keywords:
diagnostics
staged trees
Chain Event Graphs
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2.5
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