On estimation of a mixture of normal density functions

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 which has interesting properties for density approximations. The question of the completeness of normal density functions for the approximation of the class of continuous probability density functions is analyzed.