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- W2332085823 abstract "This paper describes a methodology that optimally reduces failure probability prediction uncertai nty caused by errors associated with representing population statistics with finite sample statistics. The methodology use s probabilistic sensitivity calculations, confidence interval sensitivity calculations , and cost per sample to create a measure that represents the cost effectiveness of further rando m variable sampling. This measure is called the cost -based S ample Sensitivity Index (SSI c) and e ach additional sample has an index associated with it. Maximizing the sum of these i ndexes for a fixed test budget size leads to an optimall y determined allocation of future sampling. Thus, with a given test budget, testing can be cost effectively allocated t o the variables that most reduce confidence bounds on failure probability calculations . This methodolog y is demonstrated on a 7 -parameter single degr ee of freedom oscillator and a 5 paramter rotating disk maximum radial stress problem . Validation of the efficiency is conducted by comparing the uncertainty reduction associated with the SSI c approach to othe r potential sampling allocations." @default.
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- W2332085823 date "2002-04-22" @default.
- W2332085823 modified "2023-09-25" @default.
- W2332085823 title "Optimally Reducing Statistical Uncertainty through a Cost-Based Sample Sensitivity Index" @default.
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- W2332085823 doi "https://doi.org/10.2514/6.2002-1275" @default.
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