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• W2214590087 abstract "Gaussian concentration graphical models are one of the most popular models for sparse covariance estimation with high-dimensional data. In recent years, much research has gone into development of methods which facilitate Bayesian inference for these models under the standard $G$-Wishart prior. However, convergence properties of the resulting posteriors are not completely understood, particularly in high-dimensional settings. In this paper, we derive high-dimensional posterior convergence rates for the class of decomposable concentration graphical models. A key initial step which facilitates our analysis is transformation to the Cholesky factor of the inverse covariance matrix. As a by-product of our analysis, we also obtain convergence rates for the corresponding maximum likelihood estimator." @default.
• W2214590087 created "2016-06-24" @default.
• W2214590087 creator A5002453129 @default.
• W2214590087 creator A5047432897 @default.
• W2214590087 creator A5070014481 @default.
• W2214590087 date "2015-01-01" @default.
• W2214590087 modified "2023-09-26" @default.
• W2214590087 title "High dimensional posterior convergence rates for decomposable graphical models" @default.
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• W2214590087 doi "https://doi.org/10.1214/15-ejs1084" @default.
• W2214590087 hasPublicationYear "2015" @default.
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