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Thursday, August 6, 2020 | History

2 edition of User"s description of second-order error propagation (SOERP) computer code for statistically independent variables found in the catalog.

User"s description of second-order error propagation (SOERP) computer code for statistically independent variables

Neil D Cox

User"s description of second-order error propagation (SOERP) computer code for statistically independent variables

by Neil D Cox

  • 61 Want to read
  • 39 Currently reading

Published by Dept. of Energy, Idaho Operations Office, Idaho National Engineering Laboratory, for sale by the National Technical Information Service] in [Idaho Falls, Idaho], [Springfield, Va .
Written in English

    Subjects:
  • Error functions,
  • Engineering -- Statistical methods

  • Edition Notes

    Statementby Neil D. Cox, Charles F. Miller, EG&G Idaho, Inc. ; prepared for the Department of Energy, Idaho Operations Office..
    SeriesTREE ; 1216
    ContributionsMiller, Charles Frederick, 1940- joint author, United States. Dept. of Energy. Idaho Operations Office, Idaho National Engineering Laboratory, EG & G Idaho
    The Physical Object
    Pagination18 p. ;
    Number of Pages18
    ID Numbers
    Open LibraryOL14884413M

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User"s description of second-order error propagation (SOERP) computer code for statistically independent variables by Neil D Cox Download PDF EPUB FB2

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Book Description. GIS users and professionals are aware that the accuracy of GIS results cannot be naively based on the quality of the graphical output. Data stored in a GIS will have been collected or measured, classified, generalised, interpreted or estimated, and Users description of second-order error propagation book all cases this allows the introduction of errors.

Restarting propagation may be necessary if a propagation job is disabled automatically due to errors. These types of changes affect all propagation jobs on the database link for the source queue. The user who owns the source queue is the user who propagates events.

This user must have the necessary privileges to propagate events. A general function for the calculation of uncertainty propagation by first-/second-order Taylor expansion and Monte Carlo simulation including covariances.

Input data can be any symbolic/numeric differentiable expression and data based on summaries (mean & s.d.) or sampled from distributions. Uncertainty propagation is based completely on matrix calculus accounting for full covariance structure.

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Addeddate Documentid Identifier nasa_techdoc_ Identifier-ark ark://t2dp56 Ocr ABBYY FineReader Ppi. be approximated to second order using the theory of Lie algebras and Lie groups. We then show how errors that are small (but not so small that linearization is valid) can be propagated by a recursive formula derived here.

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Using a compact matrix calculus notation, first and second order Taylor series expansions of the model equations are derived and used to estimate the mean and variance-covariance properties of.

In this paper we show how errors propagated by convolution on the Euclidean motion group, SE(3), can be approximated to second order using the theory of Lie algebras and Lie groups. We then show how errors that are small (but not so small that linearization is.

Basic formula for propagation of errors The formulas derived in this tutorial for each different mathematical operation are based on taking the partial derivative of a function with respect to each variable that has uncertainty.

Physical Sciences 2 Harvard University, Fall There is good news, though. The more complicated expression in equation (3) has a very nice feature.

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