AbstractWe present a homogenized nonlinear filter for multi-timescale systems, which allows the reduction of the dimension of filtering equation. We prove that the actual nonlinear filter converges to our homogenized filter. This is achieved by a suitable asymptotic expansion of the dual of the Zakai equation, and probabilistically representing the correction terms with the help of backward doubly-stochastic differential equations. This homogenized filter provides a rigorous mathematical basis for the development of reduced-dimension nonlinear filters for multiscale systems. A filtering scheme, based on the homogenized filtering equation and the technique of importance sampling, is applied to a chaotic multiscale system in Lingala et al. [1...
"October, 1982."Bibliography: leaf [7]Air Force Office of Scientific Research Grant No. AF-AFOSR 82-...
Faculty of Science, School of Statistics & Actuarial Science, MSC DissertationThis thesis follows a ...
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of ...
AbstractWe present a homogenized nonlinear filter for multi-timescale systems, which allows the redu...
State or signal estimation of stochastic systems based on measurement data is an important problem i...
AbstractWe present an efficient particle filtering algorithm for multi-scale systems, that is adapte...
In this dissertation, we study the implementation of nonlinear filtering algorithms that can be used...
In this paper, we study filtering of multiscale dynamical systems with model error arising from lim-...
A series of novel filters for probabilistic inference that propose an alternative way of performing ...
In this paper, the average principles and the nonlinear filtering problems of multiscale McKean-Vlas...
The Problem of nonlinear filtering is studied for a class of diffusions whose statistics depend peri...
AbstractThe multiresolution analysis (MRA) strategy for the reduction of a nonlinear differential eq...
In this paper, we investigate a nonlinear ¯ltering problem with correlated noises, bounded coe±cient...
International audienceThis paper presents a new nonlinear filtering algorithm that is shown to outpe...
peer reviewedThis paper deals with the problem of estimating a state process, the measurements of w...
"October, 1982."Bibliography: leaf [7]Air Force Office of Scientific Research Grant No. AF-AFOSR 82-...
Faculty of Science, School of Statistics & Actuarial Science, MSC DissertationThis thesis follows a ...
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of ...
AbstractWe present a homogenized nonlinear filter for multi-timescale systems, which allows the redu...
State or signal estimation of stochastic systems based on measurement data is an important problem i...
AbstractWe present an efficient particle filtering algorithm for multi-scale systems, that is adapte...
In this dissertation, we study the implementation of nonlinear filtering algorithms that can be used...
In this paper, we study filtering of multiscale dynamical systems with model error arising from lim-...
A series of novel filters for probabilistic inference that propose an alternative way of performing ...
In this paper, the average principles and the nonlinear filtering problems of multiscale McKean-Vlas...
The Problem of nonlinear filtering is studied for a class of diffusions whose statistics depend peri...
AbstractThe multiresolution analysis (MRA) strategy for the reduction of a nonlinear differential eq...
In this paper, we investigate a nonlinear ¯ltering problem with correlated noises, bounded coe±cient...
International audienceThis paper presents a new nonlinear filtering algorithm that is shown to outpe...
peer reviewedThis paper deals with the problem of estimating a state process, the measurements of w...
"October, 1982."Bibliography: leaf [7]Air Force Office of Scientific Research Grant No. AF-AFOSR 82-...
Faculty of Science, School of Statistics & Actuarial Science, MSC DissertationThis thesis follows a ...
Particle filters are a popular and flexible class of numerical algorithms to solve a large class of ...