Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Conditional moment generating functions for integrals and stochastic integrals
Author: Charalambous, C.
Elliott, R.
Krishnamurthy, V.
Citation: Siam Journal on Control and Optimization, 2004; 42(5):1578-1603
Publisher: Siam Publications
Issue Date: 2004
ISSN: 0363-0129
Statement of
C. D. Charalambous, R. J. Elliott, and V. Krishnamurthy
Abstract: In this paper we present two methods for computing filtered estimates for moments of integrals and stochastic integrals of continuous-time nonlinear systems. The first method utilizes recursive stochastic partial differential equations. The second method utilizes conditional moment generating functions. An application of these methods leads to the discovery of new classes of finite-dimensional filters. For the case of Gaussian systems the recursive computations involve integrations with respect to Gaussian densities, while the moment generating functions involve differentiations of parameter dependent ordinary stochastic differential equations. These filters can be used in Volterra or Wiener chaos expansions and the expectation-maximization algorithm. The latter yields maximum-likelihood estimates for identifying parameters in state space models.
Description: © 2003 Society for Industrial and Applied Mathematics
RMID: 0020065574
DOI: 10.1137/S036301299833327X
Appears in Collections:Mathematical Sciences publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.