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Mixture decomposition via the simulated annealing algorithm

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  • S. Ingrassia

Abstract

This paper presents the problem of the evaluation of the maximum likelihood estimator, when the likelihood function has multiple maxima, using the stochastic algorithm called ‘simulated annealing’. Analysis of the particular case of the decomposition of a mixture of five univariate normal distributions shows the properties of this methodology with respect to the E—M algorithm. The results are compared considering some distance measures between the estimated distribution functions and the true one.

Suggested Citation

  • S. Ingrassia, 1991. "Mixture decomposition via the simulated annealing algorithm," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 7(4), pages 317-325, December.
    • Handle: RePEc:wly:apsmda:v:7:y:1991:i:4:p:317-325
    DOI: 10.1002/asm.3150070403
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    Cited by:

    1. Hien Nguyen & Geoffrey McLachlan, 2015. "Maximum likelihood estimation of Gaussian mixture models without matrix operations," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 371-394, December.
    2. J. Vera & Rodrigo Macías & Willem Heiser, 2009. "A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 297-315, June.

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