论文标题
相关计数数据的贝叶斯稀疏协方差结构分析
Bayesian Sparse Covariance Structure Analysis for Correlated Count Data
论文作者
论文摘要
在本文中，我们提出了一个贝叶斯图形套索，用于相关的可计数数据，并将其应用于空间犯罪数据。在提议的模型中，我们假设一个高斯图形模型，用于主导犯罪潜在风险的潜在变量。为了评估提出的模型，我们确定最佳代表样品的最佳超参数。我们将提出的模型应用于潜在变量的稀疏逆协方差，并评估部分相关系数。最后，我们说明了犯罪点数据的结果，并考虑了稀疏逆协方差的估计潜在变量和部分相关系数。
In this paper, we propose a Bayesian Graphical LASSO for correlated countable data and apply it to spatial crime data. In the proposed model, we assume a Gaussian Graphical Model for the latent variables which dominate the potential risks of crimes. To evaluate the proposed model, we determine optimal hyperparameters which represent samples better. We apply the proposed model for estimation of the sparse inverse covariance of the latent variable and evaluate the partial correlation coefficients. Finally, we illustrate the results on crime spots data and consider the estimated latent variables and the partial correlation coefficients of the sparse inverse covariance.
