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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) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/s40300-019-00156-3

Abstract

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: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Diana Cole
Date Deposited: 05 Jul 2019 08:11 UTC
Last Modified: 31 Jul 2019 07:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/75204 (The current URI for this page, for reference purposes)
Cole, Diana J.: https://orcid.org/0000-0002-8109-4832
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