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Volume 13, Issue 4
Partially Observable Stochastic Optimal Control

G.-C. Wang, J. Xiong & S.-Q. Zhang

Int. J. Numer. Anal. Mod., 13 (2016), pp. 493-512.

Published online: 2016-07

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  • Abstract

This paper is a survey on some recent results in optimal control and stochastic filtering. The goal is not to cover all recent developments in control and filtering, instead we focus on maximum principle for optimality of partial information backward or forward-backward stochastic differential equations and branching particle approximation of nonlinear filtering.

  • Keywords

Branching particle system, forward-backward stochastic differential equation, numerical approximation, maximum principle, stochastic filtering.

  • AMS Subject Headings

60H10, 60H35, 91B28, 93E11, 93E20

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{IJNAM-13-493, author = {G.-C. Wang, J. Xiong and S.-Q. Zhang}, title = {Partially Observable Stochastic Optimal Control}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2016}, volume = {13}, number = {4}, pages = {493--512}, abstract = {

This paper is a survey on some recent results in optimal control and stochastic filtering. The goal is not to cover all recent developments in control and filtering, instead we focus on maximum principle for optimality of partial information backward or forward-backward stochastic differential equations and branching particle approximation of nonlinear filtering.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/449.html} }
TY - JOUR T1 - Partially Observable Stochastic Optimal Control AU - G.-C. Wang, J. Xiong & S.-Q. Zhang JO - International Journal of Numerical Analysis and Modeling VL - 4 SP - 493 EP - 512 PY - 2016 DA - 2016/07 SN - 13 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/449.html KW - Branching particle system, forward-backward stochastic differential equation, numerical approximation, maximum principle, stochastic filtering. AB -

This paper is a survey on some recent results in optimal control and stochastic filtering. The goal is not to cover all recent developments in control and filtering, instead we focus on maximum principle for optimality of partial information backward or forward-backward stochastic differential equations and branching particle approximation of nonlinear filtering.

G.-C. Wang, J. Xiong and S.-Q. Zhang. (2016). Partially Observable Stochastic Optimal Control. International Journal of Numerical Analysis and Modeling. 13 (4). 493-512. doi:
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