Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data.
Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data.
复制标题
DOI：
10.1198/jasa.2011.tm10465
复制
发表时间：
2011
影响因子：
3.7
通讯作者：
Rodriguez A
中科院分区：
文献类型：
--
作者：
Dobra A;Lenkoski A;Rodriguez A
我们介绍了有效的马尔可夫链蒙特卡罗方法的推断和模型确定的多元和矩阵变量高斯图形模型。我们的框架是基于G-Wishart先验的精度矩阵与图，可以是可分解或不可分解的。我们扩展我们的抽样算法，一类新的条件自回归模型的稀疏估计在多元格数据，特别强调空间数据的分析。这些模型在估计跨结果的相关性结构和空间相关性结构方面具有很大的灵活性，从而允许自适应平滑和空间自相关参数。我们的方法说明了使用模拟的例子和现实世界的应用程序，涉及癌症死亡率监测。补充材料与计算机代码和数据集所需的复制我们的数值结果连同其他表格的结果可在线。
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.