Journal Article

GPU-Powered Shotgun Stochastic Search for Dirichlet Process Mixtures of Gaussian Graphical Models

Mukherjee, Chiranjit, Rodriguez, Abel

2016

Overview

Gaussian graphical models (GGMs) are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of GGMs extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous subgroups. In this article, we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable GGMs. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo (MCMC) algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the MCMC algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which MCMC algorithms are too slow to be practically useful.

• Publisher: Taylor & Francis,American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America,Taylor & Francis Ltd
• Subject: Algorithms / Dirichlet problem / Dirichlet process mixture / Gaussian graphical model / Gene expression / Graph representations / Graphical processing unit / Searches / Statistical Computing / Stochastic models / Stochastic search / Studies