Parameter estimation in mixture models using evolutive algorithms
The mixture models are widely used in cases when there are elements that come from different populations, mixed in a superpopulation. There are traditional methods for the estimation of the parameters in mixture models: the Bayesian Method and the Expectation-Maximization (EM) algorithm. For that re...
| Autor Principal: | Romero-Rios, Natalia |
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| Otros Autores: | Correa, Juan Carlos |
| Formato: | info:eu-repo/semantics/article |
| Idioma: | eng |
| Publicado: |
Universidad Santo Tomás
2016
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| Materias: | |
| Acceso en línea: |
http://revistas.usta.edu.co/index.php/estadistica/article/view/2585 |
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| Sumario: |
The mixture models are widely used in cases when there are elements that come from different populations, mixed in a superpopulation. There are traditional methods for the estimation of the parameters in mixture models: the Bayesian Method and the Expectation-Maximization (EM) algorithm. For that reason, in this work we propose the use of evolutive algorithms, such as genetic algorithms. We propose an algorithm for the comparison of evolutive and traditional methods, and we illustrate the use of this algorithm with a real application. We found that the evolutive algorithms are a competitive option to estimate the parameters in mixture models in the cases when the populations in the mixture follows a gamma distribution, the weights of the populations in the mixture are even and the sam- ple size is bigger than 100 items. For the mixture of normal distributions and the estimation of the number of populations in a mixture, the traditional method is a better option than the genetic algorithm. |
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