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- W2900690841 abstract "This paper presents the application of an inferential statistical approach which combines impreciseBayesian methods with likelihood inference, to a standard software run reliability growth model. Themain idea of the approach is to divide the set of model parameters into two subsets related to fundamentally different aspects of the overall model, and to combine an imprecise Bayesian method related toone of the subsets of the model parameters with maximum likelihood estimation for the other subset. Inaccordance with the first subset and statistical data, the imprecise Bayesian model is constructed, whichprovides lower and upper predictive probability distributions depending on the second subset of parameters. These further parameters are then estimated by a maximum likelihood method. This method isapplied to a basic software run reliability growth model and it is shown to perform better than a standardmodel. Several aspects related to the method are discussed, including its advantages, its wider applicability and the possibility to include relevant expert judgements." @default.
- W2900690841 created "2018-11-29" @default.
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- W2900690841 date "2018-11-01" @default.
- W2900690841 modified "2023-09-25" @default.
- W2900690841 title "Imprecise probabilistic inference for software run reliability growth models." @default.
- W2900690841 hasPublicationYear "2018" @default.
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