An empirical Bayes procedure for the selection of Gaussian graphical models |
| |
Authors: | Sophie Donnet Jean-Michel Marin |
| |
Institution: | 1. CEREMADE, Universit?? Paris Dauphine, Paris, France 2. Institut de Math??matiques et Mod??lisation de Montpellier, Universit?? Montpellier 2, place Eug??ne Bataillon, Case Courrier 051, 34095, Montpellier cedex 5, France
|
| |
Abstract: | A new methodology for model determination in decomposable graphical Gaussian models (Dawid and Lauritzen in Ann. Stat. 21(3), 1272?C1317, 1993) is developed. The Bayesian paradigm is used and, for each given graph, a hyper-inverse Wishart prior distribution on the covariance matrix is considered. This prior distribution depends on hyper-parameters. It is well-known that the models??s posterior distribution is sensitive to the specification of these hyper-parameters and no completely satisfactory method is registered. In order to avoid this problem, we suggest adopting an empirical Bayes strategy, that is a strategy for which the values of the hyper-parameters are determined using the data. Typically, the hyper-parameters are fixed to their maximum likelihood estimations. In order to calculate these maximum likelihood estimations, we suggest a Markov chain Monte Carlo version of the Stochastic Approximation EM algorithm. Moreover, we introduce a new sampling scheme in the space of graphs that improves the add and delete proposal of Armstrong et al. (Stat. Comput. 19(3), 303?C316, 2009). We illustrate the efficiency of this new scheme on simulated and real datasets. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|