Voina O.A.
Using hidden Markov models in estimating the parameters of hierarchical systems

The method of parametric estimation for hierarchical stochastic models under incomplete observations is considered. The method is based on the features of the correlation structure of hierarchical models. The main attention is paid to the practical implementation of the method. In particular, an approach is proposed that combines analytical studies and empirical verification of the solutions. Specific examples of constructing consistent estimates of the vector parameters of the distortion function are provided and illustrated by direct calculations with numerical data of the simulation model. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: distortion function, hidden Markov model, queueing system, statistical estimation, Hierarchical systems, Parameter estimation, Stochastic models, Stochastic systems, Analytical studies, Correlation structure, Distortion functions, Hidden-Markov models, Hierarchical model, Incomplete observation, Parametric estimation, Queueing system, Statistical estimation, Stochastic-modeling, Hidden Markov models

Cite:
Voina O.A. (2021). Using hidden Markov models in estimating the parameters of hierarchical systems. Cybernetics and Systems Analysis, 57 (5), 72–83. doi: https://doi.org/10.1007/s10559-021-00398-y http://jnas.nbuv.gov.ua/article/UJRN-0001268749 [In Ukrainian].