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Doctoral Thesis
DOI
https://doi.org/10.11606/T.45.1999.tde-20210729-023926
Document
Author
Full name
Juan Esteban Alberto Ramirez Cid
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 1999
Supervisor
Title in Portuguese
Métodos Bayesianos em confiabilidade de software
Keywords in Portuguese
Inferência Estatística
Abstract in Portuguese
Nesta tese introduzimos novos modelos para confiabilidade de software assumindo processos de Poisson não homogêneos. Análises Bayesiana para os modelos propostos são consideradas usando métodos MCMC (Monte Carlo em Cadeias de Markov). Em geral, dados de confiabilidade de software envolvem opiniões de especialistas que podem ser incorporadas como distribuições a priori informativas. Consideramos superposição de processos de Poisson não-homogêneos independentes com e sem introdução de covariáveis, superposição de processos de Poisson não-homogêneos assumindo dependência e uso de processo de Poisson não-homogêneo com função intensidade Weibull-exponenciada como alternativa ao uso de uma superposição de processos não-homogêneos. Usamos métodos Bayesianos para discriminar diferentes modelos para dados de confiabilidade de software
Title in English
not available
Abstract in English
In this dissertation, we introduce new models for software reliability data assuming non-homogeneous Poisson processes. Bayesian analysis are considered for the proposed models using MCMC (Markov Chain Monte Carlo) methods. Usually, software reliability data have expert opinion which could be incorporated as informative prior distributions. We consider superposition of independent non-homogeneous Poisson processes assuming and not assuming covariates, superposition of dependent non-homogeneous Poisson processes, and the use of non-homogeneous Poisson processes with a exponentiated-Weibull form for the intensity function. We use Bayesian methods dor discrimination of the proposed models for software reliability data
 
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Publishing Date
2021-07-29
 
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