标题： 贝叶斯网络的结构精炼以实现有效的模型参数化 显示英文标题

标题： Structural Refinement of Bayesian Networks for Efficient Model Parameterisation

摘要： 许多贝叶斯网络建模应用都面临数据稀缺的问题。因此，通常需要使用专家判断来确定整个网络中条件概率表（CPTs）的参数。即使结合可用数据与专家判断，这些参数的数量通常仍然非常庞大，难以处理。为解决这一挑战，已经开发出多种CPT近似方法，以减少需要确定的参数数量和复杂性，从而完全参数化一个贝叶斯网络。本文综述了多种可在实际中使用的结构优化方法，用于在贝叶斯网络中高效地近似CPT。我们不仅介绍了每种方法的内在特性和要求，还通过一个心血管风险评估的贝叶斯网络模型的工作示例对每种方法进行了评估。最后，我们提供了实用的指导，帮助贝叶斯网络实践者在直接参数化CPT不可行时选择替代方法。 显示英文摘要

摘要： Many Bayesian network modelling applications suffer from the issue of data scarcity. Hence the use of expert judgement often becomes necessary to determine the parameters of the conditional probability tables (CPTs) throughout the network. There are usually a prohibitively large number of these parameters to determine, even when complementing any available data with expert judgements. To address this challenge, a number of CPT approximation methods have been developed that reduce the quantity and complexity of parameters needing to be determined to fully parameterise a Bayesian network. This paper provides a review of a variety of structural refinement methods that can be used in practice to efficiently approximate a CPT within a Bayesian network. We not only introduce and discuss the intrinsic properties and requirements of each method, but we evaluate each method through a worked example on a Bayesian network model of cardiovascular risk assessment. We conclude with practical guidance to help Bayesian network practitioners choose an alternative approach when direct parameterisation of a CPT is infeasible.