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摘要翻译：
采用贝叶斯方法估计高斯数据的协方差矩阵。利用高斯图模型和模型选择的思想，为协方差矩阵构造先验，协方差矩阵是所有可分解图的混合。在此之前，每个图大小的概率由用户指定，相同大小的图被分配相同的概率。以前的大多数方法都假设所有的图都是相等的概率。我们通过经验证明，在识别正确的可分解图和更有效地估计协方差矩阵方面，在图的大小上分配等概率的先验优于在所有图上分配等概率的先验。
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英文标题：
《Bayesian Covariance Matrix Estimation using a Mixture of Decomposable
Graphical Models》
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作者：
Helen Armstrong, Christopher K. Carter, Kevin F. Wong and Robert Kohn
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covariance matrix that is a mixture over all decomposable graphs. For this prior the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We show empirically that the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix.
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PDF链接：
https://arxiv.org/pdf/706.1287
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