Parameter estimation in SDEs via the Fokker–Planck equation: Likelihood function and adjoint based gradient computation.

In this paper we consider the problem of identifying parameters in stochastic differential equations. For this purpose, we transform the originally stochastic and nonlinear state equation to a deterministic linear partial differential equation for the transition probability density. We provide an appropriate likelihood cost function for parameter fitting, and derive an adjoint based approach for the computation of its gradient. [ABSTRACT FROM AUTHOR]