Add to ORKGThe generalized Gauss-Hermite-filter (GGHF) is implemented in the multivariate case. We utilize a Hermite expansion of the filter den-sity and Gauss-Hermite integration for the computation of expectation values in the time and measurement update (moment equations and Bayes formula). The algorithm is successfully applied to the Bayesian estimation of a volatility parameter, where filters based on two mo-ments (EKF, UKF, GHF) fail. Moreover, the stochastic volatility model of Scott (1987) is treated
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us t...
The stochastic filtering problem deals with the estimation of the posterior distribution of the curr...
We formulate probabilistic numerical approximations to solutions of ordinary differential equations ...
We consider a generalization of the Gauss-Hermite filter (GHF), where the filter density is represen...
Maximum likelihood estimation of the parameters of stochastic differential equations commonly used i...
The conditional Gauss–Hermite filter (CGHF) utilizes a decompo-sition of the filter density p(y1, y2...
We consider the filtering problem for a partially observable stochastic process , solution to a nonl...
In modelling financial return time series and time-varying volatility, the Gaussian and the Student-...
This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discre...
The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivari...
In recent years, stochastic partial differential equations (SPDEs) have been shown to provide a usef...
We consider the filtering problem for a partially observable stochastic process {X-n, Z(n), Y-n}(n i...
We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior...
Stochastic differential equations, Nonlinear systems, Discrete measurements, Maximum likelihood esti...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us t...
The stochastic filtering problem deals with the estimation of the posterior distribution of the curr...
We formulate probabilistic numerical approximations to solutions of ordinary differential equations ...
We consider a generalization of the Gauss-Hermite filter (GHF), where the filter density is represen...
Maximum likelihood estimation of the parameters of stochastic differential equations commonly used i...
The conditional Gauss–Hermite filter (CGHF) utilizes a decompo-sition of the filter density p(y1, y2...
We consider the filtering problem for a partially observable stochastic process , solution to a nonl...
In modelling financial return time series and time-varying volatility, the Gaussian and the Student-...
This paper introduces quasi-maximum likelihood estimator for multivariate diffusions based on discre...
The Gaussian Graphical Model (GGM) is a popular tool for incorporating sparsity into joint multivari...
In recent years, stochastic partial differential equations (SPDEs) have been shown to provide a usef...
We consider the filtering problem for a partially observable stochastic process {X-n, Z(n), Y-n}(n i...
We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior...
Stochastic differential equations, Nonlinear systems, Discrete measurements, Maximum likelihood esti...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us t...
The stochastic filtering problem deals with the estimation of the posterior distribution of the curr...
We formulate probabilistic numerical approximations to solutions of ordinary differential equations ...