Una extensión del algoritmo MIMIC mediante Cópulas

Rogelio Salinas Gutiérrez, Arturo Hernández Aguirre, Enrique Raúl Villa Diharce

Abstract


A new way of modeling probabilistic dependencies in Mutual Information Maximization for Input Clustering (MIMIC) algorithm is presented. By means of copulas it is possible to separate the dependence structure from marginal distributions in a joint distribution. The use of copulas as a mechanism for modeling distributions and its applicaction to MIMIC is illustrated on Rosenbrock test function.

Keywords


Global optimization; Evolutionary computing; Estimation of Distribution Algorithms; Copulas

References


Bacigál, T. y Komorníková, M. (2006). Fitting archimedean copulas to bivariate geodetic data. Compstat 2006 Proceedings in Computational Statistics. Physica-Verlag HD.

Cherubini, U., Luciano, E. y Vecchiato, W. (2004). Copula Methods in Finance. Wiley.

Davy, M. y Doucet, A. (2005). Copulas: a new insight into positive time-frequency distributions. Signal Processing Letters, IEEE, 10(7): 215-218.

De Bonet, J.S., Isbell, C.L. y Viola, P. (1997). MIMIC: Finding optima by estimating probability densities. Advances in Neural Information Processing Systems (9): 424-430. The MIT Press.

De-Waal, D.J. y Van-Gelder, P.H.A.J.M. (2005). Modelling of extreme wave heights and periods through copulas. Extremes 8(4): 345-356.

Genest, C. y Favre, A.C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering 12(4): 347-368.

Joe, H. (1997). Multivariate models and dependence concepts. Chapman and Hall.

Larrañaga, P., Etxeberria, R., Lozano, J.A. y Peña, J.M. (1999). Optimization by learning and simulation of bayesian and gaussian networks. Technical Report KZZA-IK-4-99, University of the Basque Country.

Larrañaga, P. y Lozano, J.A. (2002). Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers.

Monjardin, P.E. (2007). Análisis de dependencia en tiempo de falla. Tesis de maestría. Centro de Investigación en Matemáticas. Guanajuato, México.

Nelsen, R.B. (2006). An introduction to Copulas. Springer Series in Statistics.

Schölzel, C. y Friederichs, P. (2008). Multivariate non-normally distributed random variables in climate research – introduction to the copula approach. Nonlinear Processes in Geophysics 15(5): 761-772.

Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris (8): 229-231.

Trivedi, P.K. y Zimmer, D.M. (2007). Copula Modeling: An Introduction for Practitioners. Foundations and Trends in Econometrics (1). Now Publishers.




DOI: https://doi.org/10.21640/ns.v2i3.218

Refbacks

  • There are currently no refbacks.


Copyright (c) 2015 Nova Scientia

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Scope

Nova Scientia is a multidisciplinary, electronic publication that publishes twice a year in the months of May and November; it is published by the Universidad De La Salle Bajío and aims to distribute unpublished and original papers from the different scientific disciplines written by national and international researchers and academics. It does not publish reviews, bibliographical revisions, or professional applications.

Nova Scientia, year 12, issue 24, May – October 2020, is a biannual journal printed by the Universidad De La Salle Bajío, with its address: Av. Universidad 602, Col. Lomas del Campestre, C. P. 37150, León, Gto. México. Phone: (52) 477 214 3900, http://novascientia.delasalle.edu.mx/. Chief editor: Ph.D. Ramiro Rico Martínez. ISSN 2007 - 0705. Copyright for exclusive use No. 04-2008-092518225500/102, Diffusion rights via computer net 04 - 2008 – 121011584800-203 both granted by the Instituto Nacional del Derecho de Autor.

Editor responsible for updating this issue: Direction of Research Department of the Universidad De La Salle Bajío, last updated on May 15th, 2020.