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.

翻译：许多贝叶斯网络建模应用面临数据稀缺的问题。因此，通常需要借助专家判断来确定网络中条件概率表（CPTs）的参数。即使结合可用数据与专家判断，需要确定的参数数量往往也过于庞大。为应对这一挑战，目前已开发出多种CPT近似方法，这些方法减少了完全参数化贝叶斯网络所需确定参数的数量和复杂度。本文系统综述了实践中可用于高效近似贝叶斯网络中CPT的各种结构精化方法。我们不仅介绍和讨论了每种方法的内在特性与要求，还通过心血管风险评估贝叶斯网络模型的具体算例对每种方法进行了评估。最后，我们为贝叶斯网络实践者提供了实用指导，以帮助他们在直接参数化CPT不可行时选择合适的替代方法。