  An efficient technique is introduced for model inference of complex nonlinear
dynamical systems driven by noise. The technique does not require extensive
global optimization, provides optimal compensation for noise-induced errors and
is robust in a broad range %of parameters of dynamical models. It is applied to
clinically measured blood pressure signal for the simultaneous inference of the
strength, directionality, and the noise intensities in the nonlinear
interaction between the cardiac and respiratory oscillations.
