Statistical Science

Experiments in Stochastic Computation for High-Dimensional Graphical Models

Beatrix Jones, Carlos Carvalho, Adrian Dobra, Chris Hans, Chris Carter, and Mike West

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We discuss the implementation, development and performance of methods of stochastic computation in Gaussian graphical models. We view these methods from the perspective of high-dimensional model search, with a particular interest in the scalability with dimension of Markov chain Monte Carlo (MCMC) and other stochastic search methods. After reviewing the structure and context of undirected Gaussian graphical models and model uncertainty (covariance selection), we discuss prior specifications, including new priors over models, and then explore a number of examples using various methods of stochastic computation. Traditional MCMC methods are the point of departure for this experimentation; we then develop alternative stochastic search ideas and contrast this new approach with MCMC. Our examples range from low (12–20) to moderate (150) dimension, and combine simple synthetic examples with data analysis from gene expression studies. We conclude with comments about the need and potential for new computational methods in far higher dimensions, including constructive approaches to Gaussian graphical modeling and computation.

Article information

Statist. Sci., Volume 20, Number 4 (2005), 388-400.

First available in Project Euclid: 12 January 2006

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Decomposable models nondecomposable models Markov chain Monte Carlo shotgun stochastic search parallel implementation


Jones, Beatrix; Carvalho, Carlos; Dobra, Adrian; Hans, Chris; Carter, Chris; West, Mike. Experiments in Stochastic Computation for High-Dimensional Graphical Models. Statist. Sci. 20 (2005), no. 4, 388--400. doi:10.1214/088342305000000304.

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