Accession Number : ADA189787
Title : The Partially Observed Stochastic Minimum Principle.
Descriptive Note : Rept. for 30 Sep 86-30 Sep 87,
Corporate Author : ALBERTA UNIV EDMONTON DEPT OF STATISTICS AND APPLIED PROBABILITY
Personal Author(s) : Baras, John ; Elliott, Robert J ; Kohlmann, Michael
PDF Url : ADA189787
Report Date : 11 Nov 1987
Pagination or Media Count : 20
Abstract : The focus of this research is the filtering jump processes. To investigate the filtering of manifold valued processes, their approximation by random walks and Markov chains was studied. The object was to approximate a signal process by a finite-state jump process for which a finite dimensional filter is available. The Partially Observed Stochastic Minimum Principle: A minimum principle for a partially observed diffusion can be obtained by differentiating the statement that a control u* is optimal. The results on stochastic flows enable us to compute in an easy and explicit way the change in the cost due to a strong variation of an optical control. The only technical difficulty is the justification of the differentiation.
Descriptors : *STOCHASTIC CONTROL, *OPTIMIZATION, COSTS, FLOW, MARKOV PROCESSES, SIGNAL PROCESSING, STOCHASTIC PROCESSES, MATHEMATICAL FILTERS, APPROXIMATION(MATHEMATICS), OPEN LOOP SYSTEMS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE