The book addresses the problem of calculation of d-dimensional integrals (conditional expectations) in filter problems. It develops new methods of deterministic numerical integration, which can be used to speed up and stabilize filter algorithms. With the help of these methods, better estimates and predictions of latent variables are made possible in the fields of economics, engineering and physics. The resulting procedures are tested within four detailed simulation studies
General approaches to modeling, for instance using object-oriented software, lead to differential al...
Abstract. The purpose of this article is to survey some intrinsic methods for studying the stability...
There are numerous contexts where one wishes to describe the state of a randomly evolving system. Ef...
We are rarely able to fully and directly observe many phenomena which are crucial to our daily lives...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
The conditional probability density function of the state of a stochastic dynamic system represents ...
© 2003 Society for Industrial and Applied MathematicsIn this paper we present two methods for comput...
This dissertation presents five different solutions to the nonlinear filtering problem. Three filter...
The first chapter of my thesis (co-authored with David N. DeJong, Jean-Francois Richard and Roman Li...
Optimal estimation problems arise in various different settings where in-direct noisy observations a...
International audienceDigital filters are small iterative algorithms, used as basic bricks in signal...
The projection filter is a technique for approximating the solutions of optimal filtering problems. ...
This paper addresses the problem of how one can improve the performance of a non-optimal filter. Fir...
This book focuses on filtering, control and model-reduction problems for two-dimensional (2-D) syste...
This paper describes a new class of algorithms for integrating linear second order equations, and th...
General approaches to modeling, for instance using object-oriented software, lead to differential al...
Abstract. The purpose of this article is to survey some intrinsic methods for studying the stability...
There are numerous contexts where one wishes to describe the state of a randomly evolving system. Ef...
We are rarely able to fully and directly observe many phenomena which are crucial to our daily lives...
This unified treatment of linear and nonlinear filtering theory presents material previously availab...
The conditional probability density function of the state of a stochastic dynamic system represents ...
© 2003 Society for Industrial and Applied MathematicsIn this paper we present two methods for comput...
This dissertation presents five different solutions to the nonlinear filtering problem. Three filter...
The first chapter of my thesis (co-authored with David N. DeJong, Jean-Francois Richard and Roman Li...
Optimal estimation problems arise in various different settings where in-direct noisy observations a...
International audienceDigital filters are small iterative algorithms, used as basic bricks in signal...
The projection filter is a technique for approximating the solutions of optimal filtering problems. ...
This paper addresses the problem of how one can improve the performance of a non-optimal filter. Fir...
This book focuses on filtering, control and model-reduction problems for two-dimensional (2-D) syste...
This paper describes a new class of algorithms for integrating linear second order equations, and th...
General approaches to modeling, for instance using object-oriented software, lead to differential al...
Abstract. The purpose of this article is to survey some intrinsic methods for studying the stability...
There are numerous contexts where one wishes to describe the state of a randomly evolving system. Ef...