Stochastic Filtering Theory

1980 ◽  
Author(s):  
Gopinath Kallianpur
Keyword(s):  
Filtering Theory ◽  
Stochastic Filtering

scholarly journals Stochastic filtering theory

1983 ◽  
Vol 50 (3) ◽  
pp. 281
Author(s):  
Gian-Carlo Rota
Keyword(s):  
Filtering Theory ◽  
Stochastic Filtering

An overview of stochastic filtering theory

1985 ◽  
Vol 17 (2) ◽  
pp. 249-251
Author(s):  
Ioannis Karatzas
Keyword(s):  
Filtering Theory ◽  
Stochastic Filtering

Stochastic filtering theory

1979 ◽  
Vol 11 (2) ◽  
pp. 264-264
Author(s):  
G. Kallianpur
Keyword(s):  
Filtering Theory ◽  
Stochastic Filtering

scholarly journals Optimal switching problems under partial information

2015 ◽  
Vol 21 (2) ◽  
Author(s):  
Kai Li ◽  
Kaj Nyström ◽  
Marcus Olofsson
Keyword(s):  
Monte Carlo ◽  
Dynamic Programming ◽  
Value Function ◽  
Partial Information ◽  
Convergence Result ◽  
Optimal Switching ◽  
Filtering Theory ◽  
Stochastic Filtering ◽  
Observation Process ◽  
The Value Function

AbstractIn this paper, we formulate and study an optimal switching problem under partial information. In our model, the agent/manager/investor attempts to maximize the expected reward by switching between different states/investments. However, he is not fully aware of his environment and only an observation process, which contains partial information about the environment/underlying, is accessible. It is based on the partial information carried by this observation process that all decisions must be made. We propose a probabilistic numerical algorithm, based on dynamic programming, regression Monte Carlo methods, and stochastic filtering theory, to compute the value function. In this paper, the approximation of the value function and the corresponding convergence result are obtained when the underlying and observation processes satisfy the linear Kalman–Bucy setting. A numerical example is included to show some specific features of partial information.

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