ROBUST GRAPHICAL MODELING OF GENE NETWORKS USING CLASSICAL AND ALTERNATIVE t-DISTRIBUTIONS
ROBUST GRAPHICAL MODELING OF GENE NETWORKS USING CLASSICAL AND ALTERNATIVE t-DISTRIBUTIONS
复制标题
DOI：
10.1214/10-aoas410
复制
发表时间：
2011-06-01
影响因子：
1.8
通讯作者：
Drton, Mathias
中科院分区：
文献类型：
--
作者：
Finegold, Michael;Drton, Mathias
图形高斯模型已被证明是基于多变量数据探索网络结构的有用工具。基因表达研究的应用程序已经产生了很大的兴趣，在这些模型，并由此产生的最新进展包括拟合方法的发展，涉及惩罚的似然函数。在本文中，我们提倡使用多变量t-分布更强大的推理图。特别是，我们证明了惩罚似然推断结合EM算法的应用程序提供了一个计算效率高的方法来选择模型的t分布的情况下。我们考虑两个版本的多元t-分布，其中之一需要使用近似技术。对于这种分布，我们描述了一个马尔可夫链蒙特卡洛EM算法的基础上的吉布斯采样器以及一个简单的变分近似，使所得到的方法在大的问题是可行的。
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of the likelihood function. In this paper we advocate the use of multivariate t-distributions for more robust inference of graphs. In particular, we demonstrate that penalized likelihood inference combined with an application of the EM algorithm provides a computationally efficient approach to model selection in the t-distribution case. We consider two versions of multivariate t-distributions, one of which requires the use of approximation techniques. For this distribution, we describe a Markov chain Monte Carlo EM algorithm based on a Gibbs sampler as well as a simple variational approximation that makes the resulting method feasible in large problems.