Method of best successive approximations for nonlinear operators
We present a new approach to the approximation of nonlinear operators in probability spaces. The approach is based on a combination of the specific iterative procedure and the best approximation problem solution with a quadratic approximant. We show that the combination of these new techniques allow us to build a computationally efficient and flexible method. The algorithm of the method and its application to the optimal filtering of stochastic signals are given.
operator approximation functional minimization best estimation