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    Kriging and Conditional Simulation of Gaussian Field

    Source: Journal of Engineering Mechanics:;1995:;Volume ( 121 ):;issue: 002
    Author:
    Masaru Hoshiya
    DOI: 10.1061/(ASCE)0733-9399(1995)121:2(181)
    Publisher: American Society of Civil Engineers
    Abstract: A Gaussian discrete field is considered, whose mean field and covariance matrix are known a priori. When a sample observation set contaminated with noise at some finite points is obtained, the best estimators are evaluated at observation points as well as at interpolation points, based on an unbiased least error covariance procedure. To visualize a sample field, a method to simulate the Gaussian field conditional on observation, is investigated. A special case is considered, in which the observation is free of noises and an effective method of simulation is proposed, which is a step by step expansion procedure to avoid the Cholesky or modal decomposition of the covariance matrix. This method is based on the orthogonality property between the best estimator and the corresponding error. Numerical examples are demonstrated to show the potential usefulness of the simulation method, and to show that the updating procedure by observation is identical to the Kalman filter algorithm when plural sets of observation are processed.
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      Kriging and Conditional Simulation of Gaussian Field

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    contributor authorMasaru Hoshiya
    date accessioned2017-05-08T22:37:32Z
    date available2017-05-08T22:37:32Z
    date copyrightFebruary 1995
    date issued1995
    identifier other%28asce%290733-9399%281995%29121%3A2%28181%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/84184
    description abstractA Gaussian discrete field is considered, whose mean field and covariance matrix are known a priori. When a sample observation set contaminated with noise at some finite points is obtained, the best estimators are evaluated at observation points as well as at interpolation points, based on an unbiased least error covariance procedure. To visualize a sample field, a method to simulate the Gaussian field conditional on observation, is investigated. A special case is considered, in which the observation is free of noises and an effective method of simulation is proposed, which is a step by step expansion procedure to avoid the Cholesky or modal decomposition of the covariance matrix. This method is based on the orthogonality property between the best estimator and the corresponding error. Numerical examples are demonstrated to show the potential usefulness of the simulation method, and to show that the updating procedure by observation is identical to the Kalman filter algorithm when plural sets of observation are processed.
    publisherAmerican Society of Civil Engineers
    titleKriging and Conditional Simulation of Gaussian Field
    typeJournal Paper
    journal volume121
    journal issue2
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/(ASCE)0733-9399(1995)121:2(181)
    treeJournal of Engineering Mechanics:;1995:;Volume ( 121 ):;issue: 002
    contenttypeFulltext
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