Modelling discrete longitudinal data using acyclic probabilistic finite automata

DOI10.1016/j.csda.2015.02.009zbMath1468.62017OpenAlexW2001039109MaRDI QIDQ1663278

David Edwards, Smitha Ankinakatte

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.02.009

zbMATH Keywords

acyclic probabilistic finite automatadiscrete longitudinal datacontext-specific graphical modelstate merging

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

Context-specific graphical models for discrete longitudinal data

Uses Software

• MIM

Cites Work

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• Conditional independence and chain event graphs
• Estimating the dimension of a model
• On the learnability and usage of acyclic probabilistic finite automata
• Model selection for variable length Markov chains and tuning the context algorithm
• Variable length Markov chains
• Models for discrete longitudinal data.
• Introduction to Graphical Modelling
• The underlying structure of nonnested hypothesis tests
• Estimation and Modelling Repeated Patterns in High Order Markov Chains with the Mixture Transition Distribution Model

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