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Titlebook: Geostatistics; Proceedings of the T Margaret Armstrong (Secretary) Conference proceedings 1989 Springer Science+Business Media Dordrecht 19

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书目名称Geostatistics
副标题Proceedings of the T
编辑Margaret Armstrong (Secretary)
视频video
丛书名称Quantitative Geology and Geostatistics
图书封面Titlebook: Geostatistics; Proceedings of the T Margaret Armstrong (Secretary) Conference proceedings 1989 Springer Science+Business Media Dordrecht 19
出版日期Conference proceedings 1989
关键词fracture; geostatistics; morphology; statistics; quantitative geology; hydrogeology; economic geology
版次1
doihttps://doi.org/10.1007/978-94-015-6844-9
isbn_softcover978-94-015-6846-3
isbn_ebook978-94-015-6844-9Series ISSN 0924-1973 Series E-ISSN 2215-1834
issn_series 0924-1973
copyrightSpringer Science+Business Media Dordrecht 1989
The information of publication is updating

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The Internal Consistency of Models in Geostatisticso a question concerning the consistency of these models: for a given system of bivariate distributions, does a random function which could be compatible with this system, really exist? Going further, which class of covariances is compatible with a given univariate distribution? The condition under w
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Estimation de la Covariance et du Variogramme Pour une Fonction Aleatoire a Support Arborescent : Ap . gaussienne stationnaire caractérisée par différents modèles de covariance simples, nous étudions les propriétés des estimateurs classiques du demi-variogramme et de la fonction de covariance. Les particularités introduites par cette nouvelle forme de support sont inhabituelles : diminution de la
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A Bayesian Approach to Krigingbe known, the latter assumes nothing about the parameters involved in the expected function. The Bayesian approach allows the user to specify prior knowledge about model parameters as a qualified guess with uncertainties. This may be done in cases where the parameters have physical meaning. Two vers
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Generalised Cross-CovariancesCRF-K.,). Generalised cross-covariances are introduced and methods of fitting them to experimental data are discussed. An example of fitting a generalised cross-covariance to a co-simulated pair of variables is given.
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The Many Faces of Spatial Predictionn its various forms) has an advantage over other predictors in that it adapts to the quantity and quality of spatial dependence demonstrated by the data. Properties of the various methods are discussed very briefly.
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