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arXiv · 1509.06365

Expanding the Computation of Mixture Models by the use of Hermite Polynomials and Ideals

Andrew Clark
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Abstract

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)

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Andrew Clark. 2015-09-21. Expanding the Computation of Mixture Models by the use of Hermite Polynomials and Ideals. https://arxiv.org/abs/1509.06365

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