Statistics – Computation
Scientific paper
2010-03-30
Statistics
Computation
Scientific paper
A new methodology for model determination in decomposable graphical Gaussian models 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. (2009). We illustrate the efficiency of this new scheme on simulated and real datasets.
Donnet Sophie
Marin Jean-Michel
No associations
LandOfFree
An empirical Bayes procedure for the selection of Gaussian graphical models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with An empirical Bayes procedure for the selection of Gaussian graphical models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An empirical Bayes procedure for the selection of Gaussian graphical models will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-81449