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Distribución predictiva bayesiana para modelos de pruebas de vida vía MCMC

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Author Info
Carlos Javier Barrera ()
Juan Carlos Correa ()
Abstract

En el estudio de la confiabilidad es muy frecuente el desconocimiento de parámetros poblacionales; por tanto, es necesario recoger información muestral relevante para la estimación de estos a través de distribuciones de probabilidad, conocidas como distribución a priori. Los métodos bayesianos permiten incorporar opiniones subjetivas acerca de incertidumbres con respecto al parámetro o vector de parámetros de interés. La incertidumbre acerca del verdadero valor de un parámetro de interés  en la población es modelada por la función de densidad a priori (), ( 2 ). Para obtener las distribuciones predictivas bayesianas, se implementará la metodología MCMC, la cual exige calibración, diseño, implementación y validación de algoritmos apropiados.

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Article provided by REVISTA COLOMBIANA DE ESTADISTICA in its journal Revista Colombiana de Estadística.

Volume (Year): (2008)
Issue (Month): ()
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Handle: RePEc:col:000163:005198

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This page was last updated on 2009-12-23.


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