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This volume contains 22 articles based on papers presented at a workshop on Applied Stochastic Analysis held at Imperial College, London, in april 1989. They are concerned with applications of stochastic analysis--the theory of stochastic integration, martingales and Markov processes--to a variety of applied problems centred arount optimization of dynamical systems under uncertainty. Topics covered include characterization and approximation for stochastic system models, problems in stochastic control theory, optimization of systems modelled by piecewise deterministic Markov processes, properties of stochastic models in finance, and various facets of nonlinar filtering theory and system identification. The volume provides an authoritative and up-to-date view of the increasingly sophisticated interaction between probabilistic techniques and problems arising in these applications.
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