An empirical Bayes procedure for the selection of Gaussian graphical models
نویسندگان
چکیده
The last decade has witnessed the apparition of applied problems typified by very high-dimensional variables, in marketing database or gene expression studies for instance. Graphical models (Lauritzen (1996)) enable concise representations of associational relations between variables. If the graph is known, the parameters of the model are easily estimated. However, a quite challenging issue is the selection of the most appropriate graph for a given dataset. We consider this problem and the case of decomposable Gaussian graphical models (Dawid and Lauritzen (1993)).
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ورودعنوان ژورنال:
- Statistics and Computing
دوره 22 شماره
صفحات -
تاریخ انتشار 2012