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Bayesian inference for general Gaussian graphical models with application to multivariate lattice data. (English) Zbl 1234.62018
Summary: We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the \(G\)-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation in multivariate lattice data, with a special emphasis on the analysis of spatial data. These models embed a great deal of flexibility in estimating both the correlation structure across outcomes and the spatial correlation structure, thereby allowing for adaptive smoothing and spatial autocorrelation parameters. Our methods are illustrated using a simulated example and a real-world application which concerns cancer mortality surveillance. Supplementary materials with computer code and the datasets needed to replicate our numerical results together with additional tables of results are available online.

MSC:
62F15 Bayesian inference
62H10 Multivariate distribution of statistics
62H11 Directional data; spatial statistics
65C40 Numerical analysis or methods applied to Markov chains
62P10 Applications of statistics to biology and medical sciences; meta analysis
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