Add to ORKGThis paper deals with the relationship between two-dimensional parameter Gaussian random fields verifying a particular Markov property and the solutions of stochastic differential equations. In the non Gaussian case some diffusion conditions are introduced, obtaining a backward equation for the evolution of transition probability functions
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176990337.In this...
AbstractIn this paper we define the quasi-Markov property and give a complete characterization of a ...
AbstractTo every Markov process with a symmetric transition density, there correspond two random fie...
In this paper we show that the solution of a second-order stochastic differential equation with diff...
We study the behavior of the Gaussian concentration bound (GCB) under stochastic time evolution.More...
AbstractOur primary aim is to “build” versions of generalised Gaussian processes from simple, elemen...
The purpose of this paper is to prove a characterization of the conditional independence of two inde...
The purpose of this paper is to prove a characterization of the conditional independence of two inde...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176988183.The pur...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
International audienceWe study the behavior of the Gaussian concentration bound (GCB) under stochast...
Abstract. We study a class of Gaussian random elds with negative correlations. These elds are easy t...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176990337.In this...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176990337.In this...
AbstractIn this paper we define the quasi-Markov property and give a complete characterization of a ...
AbstractTo every Markov process with a symmetric transition density, there correspond two random fie...
In this paper we show that the solution of a second-order stochastic differential equation with diff...
We study the behavior of the Gaussian concentration bound (GCB) under stochastic time evolution.More...
AbstractOur primary aim is to “build” versions of generalised Gaussian processes from simple, elemen...
The purpose of this paper is to prove a characterization of the conditional independence of two inde...
The purpose of this paper is to prove a characterization of the conditional independence of two inde...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176988183.The pur...
Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert spa...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
First, we congratulate the authors for their extremely interesting work that sheds new light on Gaus...
International audienceWe study the behavior of the Gaussian concentration bound (GCB) under stochast...
Abstract. We study a class of Gaussian random elds with negative correlations. These elds are easy t...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176990337.In this...
This is the published version, also available here: http://dx.doi.org/10.1214/aop/1176990337.In this...
AbstractIn this paper we define the quasi-Markov property and give a complete characterization of a ...
AbstractTo every Markov process with a symmetric transition density, there correspond two random fie...