AbstractWe give two characterisations of the finite Markov property for Gaussian processes indexed by R, based on the covariance of these processes. Then, we use this approach, combined with the hyperbolic structure of R2+, to give prediction results for the two-parameter Wiener process. The complete identity between Green functions on [0, 1] and covariance of Markov Gaussian processes indexed by [0, 1] is also established
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...
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...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
AbstractWe derive the distribution of the first exit value for a class of symmetric real-valued Mark...
AbstractWe perform a multiscale analysis of Gaussian Markovian processes of order p on (0, 1). Namel...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...
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...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
The aim of this paper is to give a characterization of the gaussian processes which have the G-Marko...
AbstractWe derive the distribution of the first exit value for a class of symmetric real-valued Mark...
AbstractWe perform a multiscale analysis of Gaussian Markovian processes of order p on (0, 1). Namel...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
International audienceUsing multiple Wiener-Itô stochastic integrals and Malliavin calculus we study...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...
50 pagesWe introduce the notion of {\em covariance measure structure} for square integrable stochast...