  This paper presents a direct method to obtain the deterministic and
stochastic contribution of the sum of two independent sets of stochastic
processes, one of which is composed by Ornstein-Uhlenbeck processes and the
other being a general (non-linear) Langevin process. The method is able to
distinguish between all stochastic process, retrieving their corresponding
stochastic evolution equations. This framework is based on a recent approach
for the analysis of multidimensional Langevin-type stochastic processes in the
presence of strong measurement (or observational) noise, which is here extended
to impose neither constraints nor parameters and extract all coefficients
directly from the empirical data sets. Using synthetic data, it is shown that
the method yields satisfactory results.
