The influence of observation sequence features on the performance of the Bayesian hidden Markov model: A Monte Carlo simulation study

The hidden Markov model is a popular modeling strategy for describing and explaining latent process dynamics. There is a lack of information on the estimation performance of the Bayesian hidden Markov model when applied to categorical, one-level data. We conducted a simulation study to assess the ef...

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Veröffentlicht in:	PloS one 2024-12, Vol.19 (12), p.e0314444
Hauptverfasser:	Simons, Jan-Willem, Boverhof, Bart-Jan, Aarts, Emmeke
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Bayes Theorem Bayesian analysis Bayesian statistical decision theory Computer Simulation Convergence Data mining Engineering and Technology Evaluation Information processing Markov analysis Markov Chains Markov processes Models, Statistical Monte Carlo Method Monte Carlo simulation Parameter estimation Physical sciences Probability Research and analysis methods Separation
Online-Zugang:	Volltext
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