论文标题
贝叶斯关节推断多个有向无环图
Bayesian joint inference for multiple directed acyclic graphs
论文作者
论文摘要
在许多应用程序中，数据通常来自可能具有相似特征的多个组。与分别估算每个组的参数分别对几个组进行建模的联合估计方法可以更有效。我们专注于基于定向的无环图阐明数据的依赖性结构，并为多个图提出了贝叶斯关节推理方法。为了鼓励所有组的类似依赖性结构，采用了马尔可夫随机字段。我们建立了高维后分数后部的联合选择一致性，并且在共同的支持假设下显示了关节推断的益处。这是第一种贝叶斯的方法，用于估算多个有向无环图的联合估计。使用仿真研究证明了所提出的方法的性能，这表明我们的联合推理优于其他竞争对手。我们将方法应用于fMRI数据，以同时推断多个大脑功能网络。
In many applications, data often arise from multiple groups that may share similar characteristics. A joint estimation method that models several groups simultaneously can be more efficient than estimating parameters in each group separately. We focus on unraveling the dependence structures of data based on directed acyclic graphs and propose a Bayesian joint inference method for multiple graphs. To encourage similar dependence structures across all groups, a Markov random field prior is adopted. We establish the joint selection consistency of the fractional posterior in high dimensions, and benefits of the joint inference are shown under the common support assumption. This is the first Bayesian method for joint estimation of multiple directed acyclic graphs. The performance of the proposed method is demonstrated using simulation studies, and it is shown that our joint inference outperforms other competitors. We apply our method to an fMRI data for simultaneously inferring multiple brain functional networks.
