  A general method is proposed which allows one to estimate drift and diffusion
coefficients of a stochastic process governed by a Langevin equation. It
extends a previously devised approach [R. Friedrich et al., Physics Letters A
271, 217 (2000)], which requires sufficiently high sampling rates. The analysis
is based on an iterative procedure minimizing the Kullback-Leibler distance
between measured and estimated two time joint probability distributions of the
process.
