Hidden Markov models with binary dependence

DANIŞMAN Ö., UZUNOĞLU KOÇER U. Z.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, cilt.567, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 567
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.physa.2020.125668
  • Dergi Adı: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Artic & Antarctic Regions, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Markov process, Dependency assumption, Baum-Welch algorithm, Hidden Markov model, Parameter estimation, Strong earthquake, PROBABILISTIC FUNCTIONS, RECOGNITION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Hidden Markov models are widely used to model the probabilistic structures with latent variables. The main assumption of hidden Markov models is that; observation symbols are conditionally independent and identically distributed random variables. There exist some cases where this assumption may not be valid in practice. That is, an observation symbol that occurs in the current state may depend on the previous observation symbol that occurred in the previous state. In this study, a new type of hidden Markov model is introduced in which the current pair of hidden state-emitted observation symbol and the previous pair of those have a first-order Markov dependency. The proposed model is capable of capturing a possible first-order Markov dependency between the last and the previous steps of the system. In addition, it provides a better representation for the appropriate real-life problems where, if the observation symbols have conditional dependence. It is an alternative model to the classical hidden Markov model for revealing the Markov dependency between the current and the previous binary information of the system.