Identification of Continuous-Discrete Hidden Markov Models with Multiplicative Observation Noise

Andrey Borisov and Andrey Gorshenin ()
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Andrey Borisov: Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova str. 44/2, 119333 Moscow, Russia
Andrey Gorshenin: Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Vavilova str. 44/2, 119333 Moscow, Russia

Mathematics, 2022, vol. 10, issue 17, 1-20

Abstract: The paper aims to identify hidden Markov model parameters. The unobservable state represents a finite-state Markov jump process. The observations contain Wiener noise with state-dependent intensity. The identified parameters include the transition intensity matrix of the system state, conditional drift and diffusion coefficients in the observations. We propose an iterative identification algorithm based on the fixed-interval smoothing of the Markov state. Using the calculated state estimates, we restore all required system parameters. The paper contains a detailed description of the numerical schemes of state estimation and parameter identification. The comprehensive numerical study confirms the high precision of the proposed identification estimates.

Keywords: hidden markov model; multiplicative observation noise; fixed-interval smoothing; numerical integration; EM algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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