Title :
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
fDate :
5/1/2000 12:00:00 AM
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;
Journal_Title :
Automatic Control, IEEE Transactions on
