Bayesian Joint Spike-and-Slab Graphical Lasso
Bayesian Joint Spike-and-Slab Graphical Lasso
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发表时间：
2018-05
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通讯作者：
Z. Li;T. McCormick;S. Clark
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作者：
Z. Li;T. McCormick;S. Clark
在这篇文章中，我们提出了一类新的先验，用于多高斯图形模型的贝叶斯推理。我们介绍了两个流行的过程，群体图形套索和融合图形套索的贝叶斯处理，并将它们扩展到一个连续的钉子和板条框架，允许自适应收缩和模型选择同时进行。我们开发了一种EM算法，它执行快速和动态的后验模式探索。我们的方法高效地、自动地选择稀疏模型，与替代的正则化过程相比，偏差要小得多。通过仿真和两个实际数据算例验证了所提方法的有效性。
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce 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 and automatically 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.