Tallennetaan OA-artikkeli, kun julkaistuThis letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.Peer reviewe
Correlation and smoothness are terms used to describe a wide variety of random quantities. In time, ...
A novel method of analysis for nonlinear stochastic dynamical systems under Gaussian white noise exc...
We study a class of Gaussian processes for which the posterior mean, for a particular choice of data...
This paper is concerned with inferring the state of a Itô stochastic differential equation (SDE) fro...
This paper considers approximate smoothing for discretely observed non-linear stochastic differentia...
The nonlinear Gaussian Mixture Model Dynamically Orthogonal (GMM-DO) smoother for high-dimensional s...
Stochastic differential equations, Nonlinear systems, Discrete measurements, Maximum likelihood esti...
The basic ideas of the statistical linearization method lies in the replacing a non-linear system by...
AbstractIn this paper, we establish lower and upper Gaussian bounds for the probability density of t...
In this paper, we establish lower and upper Gaussian bounds for the probability density of the mild ...
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochasti...
International audienceStatistical smoothing in general non-linear non-Gaussian systems is a challeng...
This letter presents the development of novel iterated filters and smoothers that only require speci...
Stochastic differential equations for the linear fixed point, fixed interval, and fixed lag smoothin...
Many particulate systems occurring in nature and technology are adequately described by a number den...
Correlation and smoothness are terms used to describe a wide variety of random quantities. In time, ...
A novel method of analysis for nonlinear stochastic dynamical systems under Gaussian white noise exc...
We study a class of Gaussian processes for which the posterior mean, for a particular choice of data...
This paper is concerned with inferring the state of a Itô stochastic differential equation (SDE) fro...
This paper considers approximate smoothing for discretely observed non-linear stochastic differentia...
The nonlinear Gaussian Mixture Model Dynamically Orthogonal (GMM-DO) smoother for high-dimensional s...
Stochastic differential equations, Nonlinear systems, Discrete measurements, Maximum likelihood esti...
The basic ideas of the statistical linearization method lies in the replacing a non-linear system by...
AbstractIn this paper, we establish lower and upper Gaussian bounds for the probability density of t...
In this paper, we establish lower and upper Gaussian bounds for the probability density of the mild ...
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochasti...
International audienceStatistical smoothing in general non-linear non-Gaussian systems is a challeng...
This letter presents the development of novel iterated filters and smoothers that only require speci...
Stochastic differential equations for the linear fixed point, fixed interval, and fixed lag smoothin...
Many particulate systems occurring in nature and technology are adequately described by a number den...
Correlation and smoothness are terms used to describe a wide variety of random quantities. In time, ...
A novel method of analysis for nonlinear stochastic dynamical systems under Gaussian white noise exc...
We study a class of Gaussian processes for which the posterior mean, for a particular choice of data...