通过结构精简降低贝叶斯网络参数量，解决数据不足时的建模难题。
Structural Refinement of Bayesian Networks for Efficient Model Parameterisation
- 利用结构优化方法减少条件概率表参数数量
- 在心血管风险模型中验证了方法有效性
- 为数据稀缺场景提供实用参数化指导
许多贝叶斯网络建模应用面临数据稀缺问题，需依赖专家判断来确定条件概率表（CPT）参数。然而，即使结合可用数据与专家意见，待确定的参数仍可能过于庞大。为此，本文系统回顾了多种可用于高效近似贝叶斯网络中CPT的结构精简方法。我们不仅分析了各方法的内在特性与适用条件，还通过心血管风险评估贝叶斯网络的实例进行了实证评估。最终提出实践建议，帮助从业者在无法直接参数化CPT时选择合适替代方案。
原文摘要 · Abstract (English)
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.
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