Abstract

We describe a serial algorithm called feature-inclusion stochastic search, or FINCS, that uses online estimates of edge-inclusion probabilities to inform the process of Bayesian model determination in Gaussian graphical models. FINCS is compared to Metropolis-based search methods and found to be superior along a variety of dimensions, leading to more accurate and less volatile estimates of edge-inclusion probabilities and greater speed in finding good models. Though FINCS is conceived as a method for characterizing model uncertainty in moderate-dimensional problems, we also find that it performs well as a stochastic hill-climber in bigger problems. We illustrate its use on an example involving mutual-fund data, where we compare the model-averaged predictive performance of models discovered with FINCS to those discovered with the Metropolis algorithm.

Citations