Transition density estimation for stochastic differential equations via forward-reverse representations

Abstract

The general reverse diffusion equations are derived and applied to the problem of transition density estimation of diffusion processes between two fixed states. For this problem we propose density estimation based on forward-reverse representations and show that this method allows essentially better results to be achieved than the usual kernel or projection estimation based on forward representations only.