Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been implemented in various pieces of software. However, to date, methods to assess the robustness and validity of the learned, non-symmetric relationships are not available. Here, we introduce validation techniques tailored to staged tree models based on non-parametric bootstrap resampling methods and investigate their use in practical applications. In particular, we focus on the evaluation of transport services using large-scale survey data. In these types of applications, data from heterogeneous sources must be collated together. Staged trees provide a natural framework for this integration of data and its analysis. For the thorough evaluation of transport services, we further implement novel what-if sensitivity analyses for staged trees and their visualization using software.

翻译：分阶段树是一种相对新型的概率图模型，它扩展了贝叶斯网络，能够以形式化和图形化的方式解释非对称依赖模式。目前已有多种软件实现了从数据中学习此类模型的机器学习算法。然而，至今尚无评估所学非对称关系鲁棒性与有效性的方法。本文提出了一种基于非参数自助重采样方法、专为分阶段树模型定制的验证技术，并探究了其在实际应用中的使用。我们特别关注利用大规模调查数据评估交通服务的场景。在这类应用中，来自异构源的数据需被整合在一起，而分阶段树为此类数据整合与分析提供了天然框架。为全面评估交通服务，我们进一步实现了针对分阶段树的新型假设分析及其软件可视化方法。