Titlebook: An Introduction to Optimal Control of FBSDE with Incomplete Information; Guangchen Wang,Zhen Wu,Jie Xiong Book 2018 The Author(s), under e

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An Introduction to Optimal Control of FBSDE with Incomplete Information978-3-319-79039-8Series ISSN 2191-8198 Series E-ISSN 2191-8201

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Die vorsokratischen Philosophen with incomplete information. We first state a theorem on the stochastic filtering of a general stochastic process. The proof of that result can be found in Liptser and Shiyayev [49], so we omit it here. Then, we apply this result to the stochastic filtering for the solutions to BSDEs in Section . and to those for FBSDEs in Section ..

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Die vorsokratischen Philosophene convex variation and the duality technique, we derive a stochastic maximum principle and two verification theorems for optimality of Problem A. As an application of the optimality conditions, we solve explicitly an LQ optimal control problem and a cash management problem.

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https://doi.org/10.1007/978-3-476-03507-3: signal and observation. The signal process is what we want to estimate based on the observation which provides the information we can use. Kalman–Bucy filtering is the most successful result in linear filtering theory, which was obtained by Kalman and Bucy [38]. Nonlinear filtering is much more di

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Die vorsokratischen Philosophen with incomplete information. We first state a theorem on the stochastic filtering of a general stochastic process. The proof of that result can be found in Liptser and Shiyayev [49], so we omit it here. Then, we apply this result to the stochastic filtering for the solutions to BSDEs in Section . a

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Die vorsokratischen Philosophene convex variation and the duality technique, we derive a stochastic maximum principle and two verification theorems for optimality of Problem A. As an application of the optimality conditions, we solve explicitly an LQ optimal control problem and a cash management problem.

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Die vorsokratischen Philosophen Problem B introduced in Section .. For simplicity, we take the dimensions .. Using a direct method and a Malliavin derivative method, we establish two versions of the stochastic maximum principle for the characterization of the optimal control. To demonstrate the applicability, we work out an illus

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