Abstract. The solution of the stochastic filtering problem is approximated using Clark’s robust representation approach [1]. The ensuing approximation is shown to coincide with the time marginals of solutions of a certain McKean-Vlasov type process. The result leads to a representation of the solution of the stochastic filtering problem as a limit of empirical distributions of systems of equally weighted particles. A similar representation has been introduced by Del Moral and Miclo in [9] in the context of Feynman-Kac formulae. The representation introduced below differs from the one introduced in [9] as it involves processes with no jumps
This report presents a review of recent non-linear and robust filtering results for stochastic syste...
This paper is concerned with numerical approximations for a class of nonlinear stochastic partial di...
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...
The solution of the stochastic filtering problem is approximated using Clark's robust representation...
In this paper, the average principles and the nonlinear filtering problems of multiscale McKean-Vlas...
We introduce a weighted particle representation for the solution of the filtering problem based on a...
This work is focused on the problem of filtering of random processes and on the construction of a st...
We are rarely able to fully and directly observe many phenomena which are crucial to our daily lives...
Herein, we consider direct Markov chain approximations to the Duncan-Mortensen-Zakai equations for n...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
: We propose to study the sensitivity of the optimal filter to its initialization, by looking at the...
We provide analytical approximations for the law of the solutions to a certain class of scalar McKea...
In this article the Feynman-Kac formula is obtained for a Markov process (X t) whose transition prob...
This report presents a review of recent non-linear and robust filtering results for stochastic syste...
This thesis studies different aspects of the linear and the nonlinear stochastic filtering problem. ...
This report presents a review of recent non-linear and robust filtering results for stochastic syste...
This paper is concerned with numerical approximations for a class of nonlinear stochastic partial di...
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...
The solution of the stochastic filtering problem is approximated using Clark's robust representation...
In this paper, the average principles and the nonlinear filtering problems of multiscale McKean-Vlas...
We introduce a weighted particle representation for the solution of the filtering problem based on a...
This work is focused on the problem of filtering of random processes and on the construction of a st...
We are rarely able to fully and directly observe many phenomena which are crucial to our daily lives...
Herein, we consider direct Markov chain approximations to the Duncan-Mortensen-Zakai equations for n...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
: We propose to study the sensitivity of the optimal filter to its initialization, by looking at the...
We provide analytical approximations for the law of the solutions to a certain class of scalar McKea...
In this article the Feynman-Kac formula is obtained for a Markov process (X t) whose transition prob...
This report presents a review of recent non-linear and robust filtering results for stochastic syste...
This thesis studies different aspects of the linear and the nonlinear stochastic filtering problem. ...
This report presents a review of recent non-linear and robust filtering results for stochastic syste...
This paper is concerned with numerical approximations for a class of nonlinear stochastic partial di...
ii We propose new methods to improve nonlinear filtering and robust estimation algorithms. In the fi...