A comparison of methods for representing sparsely sampled random quantities.

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Description

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation ... continued below

Physical Description

66 p.

Creation Information

Romero, Vicente Jose; Swiler, Laura Painton; Urbina, Angel & Mullins, Joshua September 1, 2013.

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This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this report can be viewed below.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Physical Description

66 p.

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  • Report No.: SAND2013-4561
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 1096268
  • Archival Resource Key: ark:/67531/metadc829831

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Creation Date

  • September 1, 2013

Added to The UNT Digital Library

  • May 19, 2016, 9:45 a.m.

Description Last Updated

  • June 17, 2016, 3:05 p.m.

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Romero, Vicente Jose; Swiler, Laura Painton; Urbina, Angel & Mullins, Joshua. A comparison of methods for representing sparsely sampled random quantities., report, September 1, 2013; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc829831/: accessed September 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.