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.
Develops new methods of deterministic numerical integration
Derives and describes state of the art filter algorithms
Presents methods of deterministic numerical integration
Dominik Ballreich
Kalman filter Recursive Bayesian estimation State-space models Smolyak cubature Numerical integration Cubature Kalman filter Maximum Likelihood estimation Deterministic numerical integration Univariate non-stationary growth model Six-dimentional coordinated turn model Lorenz model Ginzburg-Landau model Optimization and stabilization of cubature rules Smolyak cubature rules with an approximate degree of exactness Filtering in dynamical systems