Feature-Inclusion Stochastic Search for Gaussian Graphical Models

We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to guide Bayesian model determination in Gaussian graphical models. FINCS is compared to MCMC, to Metropolis-based search methods, and to the popular lasso; it is found to be superior along a variety of dimensions, leading to better sets of discovered models, greater speed and stability, and reasonable estimates of edge-inclusion probabilities. We illustrate FINCS on an example involving mutual-fund data, where we compare the model-averaged predictive performance of models discovered with FINCS to those discovered by competing methods.