  A new optimization procedure for the estimation of Kramers-Moyal coefficients
from stationary, one-dimensional, Markovian time series data is presented. The
method takes advantage of a recently reported approach that allows to calculate
exact finite sampling interval effects by solving the adjoint Fokker-Planck
equation. Therefore it is well suited for the analysis of sparsely sampled time
series. The optimization can be performed either making a parametric ansatz for
drift and diffusion functions or also parameter free. We demonstrate the power
of the method in several numerical examples with synthetic time series.
