Model selection and accounting for model uncertainty in graphical models using Occam's window

Model selection and accounting for model uncertainty in graphical models using Occam's window (1993)

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by David Madigan , Adrian E. Raftery

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Datum	Value	Source
TITLE	Model selection and accounting for model uncertainty in graphical models using Occam's window	INFERENCE
AUTHOR NAME	David Madigan	SVM HeaderParse 0.2
AUTHOR AFFIL	University ofWashington	SVM HeaderParse 0.2
AUTHOR NAME	Adrian E. Raftery	SVM HeaderParse 0.2
AUTHOR AFFIL	grateful to Gregory Cooper	SVM HeaderParse 0.2
ABSTRACT	We consider the problem of model selection and accounting for model uncertainty in high-dimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic P-values leading to the selection of a single model � inference is then conditional on the selected model. The sampling properties of such a strategy are complex, and the failure to take account of model uncertainty leads to underestimation of uncertainty about quantities of interest. In principle, a panacea is provided by the standard Bayesian formalism which averages the posterior distributions of the quantity ofinterest under each of the models, weighted by their posterior model probabilities. Furthermore, this approach is optimal in the sense of maximising predictive ability. However, this has not been used in practice because computing the posterior model probabilities is hard and the number of models is very large (often greater than 1011). We argue that the standard Bayesian formalism is unsatisfactory and we propose an alternative Bayesian approach that, we contend, takes full account of the true model uncertainty byaveraging overamuch smaller set of models. An e cient search algorithm is developed for nding these models. We consider two classes of graphical models that arise in expert systems: the recursive causal models and the decomposable	SVM HeaderParse 0.2
YEAR	1991	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report no. 213	INFERENCE
CITATIONS	51 found	ParsCit 1.0