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- W2804912909 abstract "In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce fully Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shrinkage and model selection simultaneously. We develop an EM algorithm that performs fast and dynamic explorations of posterior modes. Our approach selects sparse models efficiently with substantially smaller bias than would be induced by alternative regularization procedures. The performance of the proposed methods are demonstrated through simulation and two real data examples." @default.
- W2804912909 created "2018-06-01" @default.
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- W2804912909 date "2018-05-18" @default.
- W2804912909 modified "2023-09-27" @default.
- W2804912909 title "Bayesian Joint Spike-and-Slab Graphical Lasso" @default.
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