摘要：Traditionally, to get the parameters of a distribution function with the maximum likelihood method is usually equaled to zero the derivative of the logarithm of the likelihood function and then the resulting non-linear system of equations is solved. The popularity of the procedure is due to its simplicity; however, when the likelihood function is not regular enough, can lead to obtain a value very far away from the maximum sought. This document presents the use of a genetic algorithm that allows to find the parameters of the distribution function by directly maximizing the likelihood function, or its logarithm, without need to resort to the derivative of the logarithms of the function. The results are compared with those obtained using a software frequently used in Mexico, for the case functions Gumbel and Gumbel of two populations.
其他摘要：Traditionally, to get the parameters of a distribution function with the maximum likelihood method is usually equaled to zero the derivative of the logarithm of the likelihood function and then the resulting non-linear system of equations is solved. The popularity of the procedure is due to its simplicity; however, when the likelihood function is not regular enough, can lead to obtain a value very far away from the maximum sought. This document presents the use of a genetic algorithm that allows to find the parameters of the distribution function by directly maximizing the likelihood function, or its logarithm, without need to resort to the derivative of the logarithms of the function. The results are compared with those obtained using a software frequently used in Mexico, for the case functions Gumbel and Gumbel of two populations.
关键词：Genetic Algorithm;Máximum likelihood;Gumbel Function;Gumbel of two populations;Optimization;Algoritmo genético;Máxima verosimilitud;Función Gumbel;Gumbel de dos poblaciones;Optimización
其他关键词：Genetic Algorithm; Máximum likelihood; Gumbel Function; Gumbel of two populations; Optimization
