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In traditional Bayesian software reliability models, it was assume that all probabilities are precise. In practical applications
the parameters of the probability distributions are often under uncertainty due to strong dependence on subjective information
of experts’ judgments on sparse statistical data. In this paper, a quasi-Bayesian software reliability model using interval-valued
probabilities to clearly quantify experts’ prior beliefs on possible intervals of the parameters of the probability distributions
is presented. The model integrates experts’ judgments with statistical data to obtain more convincible assessments of software
reliability with small samples. For some actual data sets, the presented model yields better predictions than the Jelinski-Moranda
(JM) model using maximum likelihood (ML).
Project supported by the National High-Technology Research and Development Program of China (Grant Nos.2006AA01Z187, 2007AA040605) 相似文献
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