Diffusion Process with Evolution and its Parameter Estimation

Diffusion Process with Evolution and its Parameter Estimation

A discrete Markov process in an asymptotic diffusion environment with a uniformly ergodic embedded Markov chain can be approximated by an Ornstein–Uhlenbeck process with evolution. The drift parameter estimation is obtained using the stationarity of the Gaussian limit process.

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Veröffentlicht in:Cybernetics and systems analysis 2020-09, Vol.56 (5), p.732-738
Hauptverfasser: Koroliuk, V. S., Koroliouk, D., Dovgyi, S. O.
Format: Artikel
Sprache:eng
Schlagworte:
Artificial Intelligence
Control
Drift estimation
Evolution
Gaussian process
Markov chains
Markov processes
Mathematics
Mathematics and Statistics
Parameter estimation
Processor Architectures
Software Engineering/Programming and Operating Systems
Systems Theory
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Beschreibung
Zusammenfassung:A discrete Markov process in an asymptotic diffusion environment with a uniformly ergodic embedded Markov chain can be approximated by an Ornstein–Uhlenbeck process with evolution. The drift parameter estimation is obtained using the stationarity of the Gaussian limit process.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-020-00293-y