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Abstract Details
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Activity Number:
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295
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302725 |
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Title:
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On Bayesian Inference and Prediction for Computer Software
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Author(s):
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Nuria Torrado*+ and Rosa E. Lillo and Michael P. Wiper
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Companies:
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Universidad Carlos III and Universidad Carlos III and Universidad Carlos III
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Address:
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Calle Madrid, 126, Madrid, 28903 , Spain
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Keywords:
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Bayesian analysis ;
software reliability ;
nonhomogeneous Poisson processes
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Abstract:
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The main purposes of this talk is to describe statistical inference and prediction for software reliability models in the presence of covariate information. In particular, we develop a semi-parametric, Bayesian model to estimate the numbers of software failures over various time periods when it is assumed that the software is changed after each time period. Goodness-of-fit testing of the model are developed using a deviance information criterion, and predictive inferences on future failures are shown. Real life examples are presented to illustrate the new model.
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