  Mixture models have found uses in many areas. To list a few: unsupervised
learning, empirical Bayes, latent class and trait models. The current
applications of mixture models to empirical data is limited to computing a
mixture model from the same parametric family, e.g. Gaussians or Poissons. In
this paper it is shown that by using Hermite polynomials and ideals, the
modeling of a mixture process can be extended to include different families in
terms of their cumulative distribution functions (cdfs)
