The aim of this paper is to give a characterization of the gaussian processes which have the G-Markov property as stochastic integrals with respect to a Wiener process. This is done by a generalization of the known result for the positive quadrant .These results allow us to find directly the structure of the covariance function of the "bien markoviens" processes intoduced by Etienne Carna
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
We investigate the stochastic processes obtained as the fractional Riemann-Liouville integral of ord...
This paper deals with the relationship between two-dimensional parameter Gaussian random fields veri...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
AbstractWe give two characterisations of the finite Markov property for Gaussian processes indexed b...
We give two characterisations of the finite Markov property for Gaussian processes indexed by , base...
AbstractWe give two characterisations of the finite Markov property for Gaussian processes indexed b...
The purpose of this paper is to get a canonical representation of Gaussian processes which are equiv...
We develop a general theory for stochastic integrals of generalized stochastic processes X(t), depen...
We develop a general theory for stochastic integrals of generalized stochastic processes X(t), depen...
We develop a stochastic analysis for a Gaussian process $X$ with singular covariance by an intrinsic...
AbstractOur primary aim is to “build” versions of generalised Gaussian processes from simple, elemen...
In this paper we examine the characterization of multivariate reciprocal stationary Gaussian process...
In development of stochastic analysis in a Banach space one of the main problem is to establish the ...
International audienceStochastic integration with respect to Gaussian processes has raised strong in...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
We investigate the stochastic processes obtained as the fractional Riemann-Liouville integral of ord...
This paper deals with the relationship between two-dimensional parameter Gaussian random fields veri...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
AbstractWe give two characterisations of the finite Markov property for Gaussian processes indexed b...
We give two characterisations of the finite Markov property for Gaussian processes indexed by , base...
AbstractWe give two characterisations of the finite Markov property for Gaussian processes indexed b...
The purpose of this paper is to get a canonical representation of Gaussian processes which are equiv...
We develop a general theory for stochastic integrals of generalized stochastic processes X(t), depen...
We develop a general theory for stochastic integrals of generalized stochastic processes X(t), depen...
We develop a stochastic analysis for a Gaussian process $X$ with singular covariance by an intrinsic...
AbstractOur primary aim is to “build” versions of generalised Gaussian processes from simple, elemen...
In this paper we examine the characterization of multivariate reciprocal stationary Gaussian process...
In development of stochastic analysis in a Banach space one of the main problem is to establish the ...
International audienceStochastic integration with respect to Gaussian processes has raised strong in...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
We investigate the stochastic processes obtained as the fractional Riemann-Liouville integral of ord...
This paper deals with the relationship between two-dimensional parameter Gaussian random fields veri...