Title :
`Unobserved´ Monte Carlo method for identification of partially observed nonlinear state space systems. Part II. Counting process observations
Author_Institution :
Sch. of Electr. Eng. & Telecommun., New South Wales Univ., Kensington, NSW, Australia
Abstract :
For Part I see Technical Report, School of Elec. Eng., Univ. N.S.W. (2000). In this paper we present a simple simulation method for generating an approximate likelihood function for fitting partially observed (with counting process observations) nonlinear stochastic differential equations. We discuss the use of the method to generate approximate maximum likelihood estimators. We also mention methods based on density evolution equations
Keywords :
Monte Carlo methods; differential equations; maximum likelihood estimation; nonlinear systems; observability; state estimation; state-space methods; Monte Carlo method; counting process observations; density evolution equation; identification; maximum likelihood estimation; partially observed nonlinear systems; state space systems; stochastic differential equations; Australia; Differential equations; Ear; Encoding; Maximum likelihood estimation; Monte Carlo methods; State-space methods; Stochastic processes; System identification; White noise;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2000.912214
