Abstract
The problem of (pathwise) large deviations for conditionally continuous Gaussian processes is investigated. The theory of large deviations for Gaussian processes is extended to the wider class of random processes -- the conditionally Gaussian processes. The estimates of level crossing probability for such processes are given as an application.
Highlights
In this paper we study some large deviations principles for conditionally continuous Gaussian processes
The aim of this paper is to extend the theory of large deviations for Gaussian processes to a wider class of www.vmsta.org
We briefly recall some main facts on large deviations principles and reproducing kernel Hilbert spaces for Gaussian processes we are going to use
Summary
In this paper we study some large deviations principles for conditionally continuous Gaussian processes. The aim of this paper is to extend the theory of large deviations for Gaussian processes to a wider class of. The theory of large deviations for Gaussian processes and for conditioned Gaussian processes is already well developed. The extension of this theory is possible thanks to the results obtained by Chaganty in [8].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
