论文标题
校正的分数方法用于估计具有错误的节点的贝叶斯网络
Corrected score methods for estimating Bayesian networks with error-prone nodes
论文作者
论文摘要
通过使用嘈杂的流式细胞仪数据推断细胞信号网络的动机，我们开发了基于易于错误数据的贝叶斯网络的推断程序。基于刑罚估算方法，提出了两种推断网络节点之间因果关系的方法，该方法解释了测量误差并鼓励稀疏性。我们讨论了所提出的网络估计器的一致性，并开发了一种在惩罚估计方法中选择调谐参数的方法。进行了经验研究以比较提出的方法和一种天真的方法，该方法忽略了测量误差与合成数据的应用和单细胞流式细胞仪数据的应用。
Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error-prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods and a naive method that ignores measurement error with applications to synthetic data and to single cell flow cytometry data.
