标题： 信息论评分规则用于学习加性贝叶斯网络并应用于流行病学 显示英文标题

标题： Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology

摘要： 贝叶斯网络建模是一种非常适合研究混乱且高度相关的数据集的方法，这些数据集在系统流行病学等领域非常常见。 从观察性数据集中学习贝叶斯网络的一种流行方法是在搜索和评分方法中识别最大后验网络。 已经提出了许多基于贝叶斯或频率论的评分方法。 从应用的角度来看，一种合适的方法应允许数据具有多种分布，并且足够稳健以实现自主运行。 计算评分的一种有前景的框架是广义线性模型。 事实上，存在快速的估计算法以及许多针对常见流行病学问题的定制解决方案。 本文的目的在于介绍一个R包abn，它实现了多种频率论评分方法，并包含一些现实的模拟实验，展示其可用性和性能。 它包括了一些功能，可以高效处理系统流行病学中常见的数据分离和调整问题。 显示英文摘要

摘要： Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many scores have been proposed both Bayesian or frequentist based. In an applied perspective, a suitable approach would allow multiple distributions for the data and is robust enough to run autonomously. A promising framework to compute scores are generalized linear models. Indeed, there exists fast algorithms for estimation and many tailored solutions to common epidemiological issues. The purpose of this paper is to present an R package abn that has an implementation of multiple frequentist scores and some realistic simulations that show its usability and performance. It includes features to deal efficiently with data separation and adjustment which are very common in systems epidemiology.