Bayesian Multi-Task Variable Selection with an Application to Differential DAG Analysis
Bayesian Multi-Task Variable Selection with an Application to Differential DAG Analysis
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DOI：
10.1080/10618600.2023.2252023
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发表时间：
2023-09-28
影响因子：
2.4
通讯作者：
Zhou，Quan
中科院分区：
文献类型：
--
作者：
Li，Guanxun;Zhou，Quan
我们研究贝叶斯多任务变量选择问题，其目标是同时为多个相关数据集选择激活变量。我们提出了一个新的变分贝叶斯算法，推广和改进了最近开发的“单效应之和”模型的王等。受生物学中的差分基因网络分析的启发，我们进一步将我们的方法扩展到多个有向非循环图形模型的联合结构学习，这是一个在计算上极具挑战性的问题。我们提出了一种新的顺序MCMC采样器，我们的多任务变量选择算法被用来快速评估每个订单的后验概率。仿真研究和真实的基因表达数据分析表明了该方法的有效性。最后，我们还证明了多任务变量选择的后验一致性结果，这为所提出的算法提供了理论保证。本文的补充材料可在网上查阅。
We study the Bayesian multi-task variable selection problem, where the goal is to select activated variables for multiple related datasets simultaneously. We propose a new variational Bayes algorithm which generalizes and improves the recently developed “sum of single effects” model of Wang et al. Motivated by differential gene network analysis in biology, we further extend our method to joint structure learning of multiple directed acyclic graphical models, a problem known to be computationally highly challenging. We propose a novel order MCMC sampler where our multi-task variable selection algorithm is used to quickly evaluate the posterior probability of each ordering. Both simulation studies and real gene expression data analysis are conducted to show the efficiency of our method. Finally, we also prove a posterior consistency result for multi-task variable selection, which provides a theoretical guarantee for the proposed algorithms. Supplementary materials for this article are available online.