Robust learning of staged tree models: A case study in evaluating transport services

Abstract

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.