Stochastic estimation of a mixture of normal density functions using an information criterion

A stochastic approximation algorithm is developed for estimating a mixture of normal density functions with unknown means and unknown variances. The algorithm minimizes an information criterion that has interesting properties for density approximations. The conditions on the convergence of this nonlinear estimation algorithm are discussed, and a numerical example is presented.