The article is devoted to the study of nonhomogeneous hidden semi-Markov models, and proposes a methodology for estimating parameters in the case where the change of state sojourn time distributions and transition probability matrices is time-limited.
The obtained results allow the model to be applied to the description of systems with time-varying structure. The proposed methodology can be used in time series analysis tasks with structural changes, as well as in optimal control problems for stochastic systems where it is important to account for changes in the process behavior over time.
[1] Yu S.-Z. Hidden Semi-Markov Models. Artificial Intelligence, 2010, 174, 215–243.
[2] Dong M., He D. A Segmental Hidden Semi-Markov Model (HSMM)-Based Diagnostics and Prognostics Framework and Methodology. Mechanical Systems and Signal Processing, 2007, 21, 2248–2266.
[3] Zen H., Tokuda K., Masuko T., Kobayashi T., Kitamura T. A Hidden Semi-Markov Model-Based Speech Synthesis System. IEICE Transactions on Information and Systems, 2007, E90-D(5), 825–834.
[4] Gujdon Y. Estimating Hidden Semi-Markov Chains from Discrete Sequences. Journal of Computational and Graphical Statistics, 2003, 12(3), 604–639.
[5] Bulla J., Bulla I. Stylized Facts of Financial Time Series and Hidden Semi-Markov Models. Computational Statistics & Data Analysis, 2006, 51, 2192–2209.
[6] Mor B., Garhwal S., Loura A. A Systematic Review of Hidden Markov Models and Their Applications. Archives of Computational Methods in Engineering, 2020, 28, 4001–4028.
[7] Ghahramani Z. An Introduction to Hidden Markov Models and Bayesian Networks. International Journal of Pattern Recognition and Artificial Intelligence, 2001, 15(1), 9–42.
[8] Scott S.L. Bayesian Methods for Hidden Markov Models. Journal of the American Statistical Association, 2002, 97(457), 337–351.
[9] Robert C.P., Ryden T., Titterington D.M. Bayesian Inference in Hidden Markov Models through Jump Markov Chain Monte Carlo. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2000, 62(1), 57–75.
[10] Rabiner L.R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 1989, 77(2), 257–286.
[11] Viterbi A.J. Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Transactions on Information Theory, 1967, 13(2), 260–269.
- ACS Style
- Malyk, I.; Ivasyuk , R. Parameter estimation of nonhomogeneous hidden semi-Markov models. Bukovinian Mathematical Journal. 2025, 13 https://doi.org/https://doi.org/10.31861/bmj2025.02.08
- AMA Style
- Malyk I, Ivasyuk R. Parameter estimation of nonhomogeneous hidden semi-Markov models. Bukovinian Mathematical Journal. 2025; 13(2). https://doi.org/https://doi.org/10.31861/bmj2025.02.08
- Chicago/Turabian Style
- Igor Malyk, Roman Ivasyuk . 2025. "Parameter estimation of nonhomogeneous hidden semi-Markov models". Bukovinian Mathematical Journal. 13 no. 2. https://doi.org/https://doi.org/10.31861/bmj2025.02.08