AI RESEARCH
Estimating Staged Event Tree Models via Hierarchical Clustering on the Simplex
arXiv CS.LG
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ArXi:2603.15568v1 Announce Type: cross Staged tree models enhance Bayesian networks by incorporating context-specific dependencies through a stage-based structure. In this study, we present a new framework for estimating staged trees using hierarchical clustering on the probability simplex, utilizing simplex basesd divergences. We conduct a thorough evaluation of several distance and divergence metrics including Total Variation, Hellinger, Fisher, and Kaniadakis; alongside various linkage methods such as Ward. D2, average, complete, and McQuitty.