Showing papers on "Mixture model published in 1981"

Experiments with Mixtures: Designs, Models and the Analysis of Mixture Data

Abstract: Preface to the Third Edition. Preface to the Second Edition. Introduction. The Original Mixture Problem: Designs and Models for Exploring the Entire Simplex Factor Space. The Use of Independent Variables. Multiple Constraints on the Component Proportions. The Analysis of Mixture Data. Other Mixture Model Forms. The Inclusion of Process Variables in Mixture Experiments. Additional Topics. Matrix Algebra, Least Squares, and the Analysis of Variance. Data Sets from Mixture Experiments with Partial Solutions. Bibliography and Index of Authors. Answers to Selected Questions. Appendix. Index.

Unsupervised learning for signal versus noise (Corresp.)

Abstract: The Bayes solution to the unsupervised sequential learning problem induced by a mixture model for the two-class signal versus noise decision problem generates a computational and storage explosion. A quasi-Bayes approximate learning procedure is proposed that avoids the computational explosion while retaining the flavor of the Bayes solution. Convergence is established and efficiency is investigated.