On the optimal stopping with incomplete data
The Kalman–Bucy continuous model of partially observable stochastic processes is considered. The problem of optimal stopping of a stochastic process with incomplete data is reduced to the problem of optimal stopping with complete data. The convergence of payoffs is proved when ε 1 → 0 , ε 2 → 0, whe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-12-01
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| Series: | Transactions of A. Razmadze Mathematical Institute |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2346809218301272 |
| Summary: | The Kalman–Bucy continuous model of partially observable stochastic processes is considered. The problem of optimal stopping of a stochastic process with incomplete data is reduced to the problem of optimal stopping with complete data. The convergence of payoffs is proved when ε 1 → 0 , ε 2 → 0, where ε 1 , and ε 2 are small perturbation parameters of the non observable and observable processes respectively. Keywords: Partially observable process, Gain function, Payoff, Stopping time, Optimal stopping |
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| ISSN: | 2346-8092 |