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Parameter Redundancy and Identifiability in Hidden Markov Models

Cole, Diana J. (2019) Parameter Redundancy and Identifiability in Hidden Markov Models. Metron, 77 (2). pp. 105-118. ISSN 0026-1424. (doi:10.1007/s40300-019-00156-3) (KAR id:75204)


Hidden Markov models are a flexible class of models that can be used to describe time series data which depends on an unobservable Markov process. As with any complex model, it is not always obvious whether all the parameters are identifiable, or if the model is parameter redundant; that is, the model can be reparameterised in terms of a smaller number of parameters. This paper considers different methods for detecting parameter redundancy and identifiability in hidden Markov models. We examine both numerical methods and methods that involve symbolic algebra. These symbolic methods require a unique representation of a model, known as an exhaustive summary. We provide an exhaustive summary for hidden Markov models and show how it can be used to investigate identifiability.

Item Type: Article
DOI/Identification number: 10.1007/s40300-019-00156-3
Uncontrolled keywords: Hidden Markov models · Identifiability · Parameter Redundancy
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Diana Cole
Date Deposited: 05 Jul 2019 08:11 UTC
Last Modified: 04 Mar 2024 18:28 UTC
Resource URI: (The current URI for this page, for reference purposes)

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