Learning hidden Markov models with unknown number of states

DOI10.1016/j.physa.2022.127047OpenAlexW4212812991MaRDI QIDQ2116568

Yanyan Li

Publication date: 17 March 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2022.127047

zbMATH Keywords

hidden Markov modelfirst hitting timespectral clustering algorithm

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• Asymptotic operating characteristics of an optimal change point detection in hidden Markov models
• The order estimation for hidden Markov models
• Linear optimal prediction and innovations representations of hidden Markov models.
• Hierarchical Dirichlet Processes
• The likelihood ratio test for the number of components in a mixture with Markov regime
• Bayesian Non-Parametric Hidden Markov Models with Applications in Genomics
• Statistical Inference for Probabilistic Functions of Finite State Markov Chains
• A Regime-Switching Model of Long-Term Stock Returns
• Variational inference for Dirichlet process mixtures

This page was built for publication: Learning hidden Markov models with unknown number of states