An expression is obtained for the likelihood function for the detection of a stochastic signal (diffusion process) in white noise. A stochastic differential equation is then obtained for the evolution of the likelihood function and the coefficients of this differential equation are related to a corresponding nonlinear filtering problem. Some extensions are noted to diffusion process signals in correlated noise and to more general stochastic signals.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33137/1/0000523.pd
Filtering and identification problems of partially observable stochastic dynamical systems has been ...
A numerical method for approximating the statistics of the solution of nonlinear stochastic systems ...
Estimation and Model Validation of Diffusion Processes Abstract The main motivation for this thesis ...
An expression is obtained for the likelihood function for the detec-tion of a stochastic signal (dif...
An expression is obtained for the likelihood function for the detection of a stochastic signal (diff...
In this paper, the Prediction-Based Estimating Functions proposed by Sørensen (1999) are generalized...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
The general nonlinear filtering or estimation problem may be described as follows. xty (0<t<T)...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
For a general stochastic signal in white noise absolute continuity is proved and the RadonNikodym de...
AbstractThe paper treats the nonlinear filtering problem for jump-diffusion processes. The optimal f...
We provide a general framework for computing the state density of a noisy system given the sequence ...
Filtering for Stochastic Evolution Equations Vít Kubelka Doctoral thesis Abstract Linear filtering p...
For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym d...
This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calcul...
Filtering and identification problems of partially observable stochastic dynamical systems has been ...
A numerical method for approximating the statistics of the solution of nonlinear stochastic systems ...
Estimation and Model Validation of Diffusion Processes Abstract The main motivation for this thesis ...
An expression is obtained for the likelihood function for the detec-tion of a stochastic signal (dif...
An expression is obtained for the likelihood function for the detection of a stochastic signal (diff...
In this paper, the Prediction-Based Estimating Functions proposed by Sørensen (1999) are generalized...
The prediction-based estimating functions proposed by (Sørensen, 1999) are generalized to facilitate...
The general nonlinear filtering or estimation problem may be described as follows. xty (0<t<T)...
The purpose of this paper is to study some statistical problems: parameter estimation, binary detect...
For a general stochastic signal in white noise absolute continuity is proved and the RadonNikodym de...
AbstractThe paper treats the nonlinear filtering problem for jump-diffusion processes. The optimal f...
We provide a general framework for computing the state density of a noisy system given the sequence ...
Filtering for Stochastic Evolution Equations Vít Kubelka Doctoral thesis Abstract Linear filtering p...
For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym d...
This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calcul...
Filtering and identification problems of partially observable stochastic dynamical systems has been ...
A numerical method for approximating the statistics of the solution of nonlinear stochastic systems ...
Estimation and Model Validation of Diffusion Processes Abstract The main motivation for this thesis ...