Fast sampling with Gaussian scale-mixture priors in high-dimensional regression.
Fast sampling with Gaussian scale-mixture priors in high-dimensional regression.
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DOI：
10.1093/biomet/asw042
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发表时间：
2016-12
期刊：
影响因子：
2.7
通讯作者：
Mallick BK
中科院分区：
文献类型：
--
作者：
Bhattacharya A;Chakraborty A;Mallick BK
本文提出了一种从一类结构化多元高斯分布中采样的有效方法。该算法只需要矩阵乘法和线性系统的解决方案。它的计算复杂度随着维数线性增长，不像现有的算法依赖于立方复杂度的Cholesky因子分解。该算法广泛适用于高斯尺度混合先验用于高维参数的设置。其有效性说明通过一个高维回归问题的回归系数的马蹄事先。其他潜在的应用程序进行了概述。
We propose an efficient way to sample from a class of structured multivariate Gaussian distributions. The proposed algorithm only requires matrix multiplications and linear system solutions. Its computational complexity grows linearly with the dimension, unlike existing algorithms that rely on Cholesky factorizations with cubic complexity. The algorithm is broadly applicable in settings where Gaussian scale mixture priors are used on high-dimensional parameters. Its effectiveness is illustrated through a high-dimensional regression problem with a horseshoe prior on the regression coefficients. Other potential applications are outlined.