2013/11/20 by David Edwards, Edwards, David, Smitha Ankinakatte +1
Computer Science · #Bayesian Modeling and Causal Inference #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Mathematics #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1311.5066
openalex publication_date 2013/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ron et al (1998) introduced a rich family of models for discrete longitudinal data, called acyclic probabilistic finite automata. These may be described as context-specific graphical models, since they are represented as directed multigraphs that embody context-specific conditional independence relations. Here we develop the methodology from a statistical modelling perspective. We show how likelihood ratio tests may be constructed using standard contingency table methods, and indicate how these may be used in model selection. We also show that the models generalize certain subclasses of conventional undirected and directed graphical models.