  A new algorithm is presented for reconstructing stochastic nonlinear
dynamical models from noisy time-series data. The approach is analytical;
consequently, the resulting algorithm does not require an extensive global
search for the model parameters, provides optimal compensation for the effects
of dynamical noise, and is robust for a broad range of dynamical models. The
strengths of the algorithm are illustrated by inferring the parameters of the
stochastic Lorenz system and comparing the results with those of earlier
research. The efficiency and accuracy of the algorithm are further demonstrated
by inferring a model for a system of five globally- and locally-coupled noisy
oscillators.
