Maximum likelihood parameter estimation from incomplete data via the sensitivity equations: the continuous-time case

Author

Charalambous, Charalambos D. ; Logothetis, Andrew

Author_Institution

Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada

Volume

45

Issue

5

fYear

2000

fDate

5/1/2000 12:00:00 AM

Firstpage

928

Lastpage

934

Abstract

This paper deals with maximum likelihood (ML) parameter estimation of continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (EM) algorithm. It is shown that the EM algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically implemented. The methodology exploits the relationships between incomplete and complete data, log-likelihood and its gradient

Keywords

continuous time systems; filtering theory; maximum likelihood estimation; nonlinear systems; probability; sensitivity analysis; stochastic systems; EM algorithm; continuous-time systems; expectation maximization algorithm; maximum likelihood estimation; nonlinear filtering; nonlinear systems; parameter estimation; probability; sensitivity analysis; stochastic systems; Filtering algorithms; Filters; Hidden Markov models; Integral equations; Maximum likelihood estimation; Nonlinear equations; Parameter estimation; Stochastic processes; Stochastic systems; System identification;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.855553

Filename

855553