cegpy：在Python中使用链事件图建模
cegpy: Modelling with Chain Event Graphs in Python
摘要：
链事件图（Chain Event Graph，CEG）是最近出现的概率图模型家族，它是对流行的贝叶斯网络（Bayesian Network，BN）家族的推广。至关重要的是，与BN不同，CEG能够在其图和统计模型中嵌入过程所表现出的不对称性。这些不对称性可能存在于条件独立关系中，也可能存在于图的结构及其底层事件空间中。结构性不对称在许多领域都很常见，可能是自然发生的（例如，被告与检察官对事件的看法），也可能是有意为之（例如，公共卫生干预）。然而，目前不存在允许用户在具有结构不对称的建模过程中利用CEG模型族的理论发展的软件。本文介绍了cegpy，第一个使用CEG学习和分析复杂过程的Python包。cegpy的关键特性是，它是任何编程语言中第一个可以对具有对称和非对称结构的流程进行建模的CEG包。cegpy包含CEG的贝叶斯模型选择和概率传播算法的实现。我们使用结构不对称的数据集来说明cegpy的功能。
英文摘要：
Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to embed, within its graph and its statistical model, asymmetries exhibited by a process. These asymmetries might be in the conditional independence relationships or in the structure of the graph and its underlying event space. Structural asymmetries are common in many domains, and can occur naturally (e.g. a defendant vs prosecutor's version of events) or by design (e.g. a public health intervention). However, there currently exists no software that allows a user to leverage the theoretical developments of the CEG model family in modelling processes with structural asymmetries. This paper introduces cegpy, the first Python package for learning and analysing complex processes using CEGs. The key feature of cegpy is that it is the first CEG package in any programming language that can model processes with symmetric as well as asymmetric structures. cegpy contains an implementation of Bayesian model selection and probability propagation algorithms for CEGs. We illustrate the functionality of cegpy using a structurally asymmetric dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
