Solved Problems in Geostatistics, Leuangthong

Автор: Remy
Название: Applied Geostatistics with SGeMS
ISBN: 1107403243 ISBN-13(EAN): 9781107403246
Издательство: Cambridge Academ
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Цена: 51750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical book provides a detailed guide to using algorithms from the Stanford Geostatistical Modeling Software (SGeMS), an open-source computer package for solving problems involving spatially related variables. Accompanied by a CD with the software, it`s a useful user-guide for Earth Science graduates, and practitioners of environmental and petroleum engineering.

Автор: Wackernagel
Название: Multivariate Geostatistics
ISBN: 3540441425 ISBN-13(EAN): 9783540441427
Издательство: Springer
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Цена: 93160.00 T
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Описание: This fully revised third edition introduces geostatistics by emphasising the multivariate aspects for scientists, engineers and statisticians. The text contains a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of 3 basic methods of multivariate analysis.

Автор: Peter Diggle; Paulo Justiniano Ribeiro
Название: Model-based Geostatistics
ISBN: 1441921931 ISBN-13(EAN): 9781441921932
Издательство: Springer
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Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Readers can reproduce most of the computational results in the book by using the authors` software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models.

Автор: Goovaerts, Pierre.
Название: Geostatistics for natural resources evaluation /
ISBN: 0195115384 ISBN-13(EAN): 9780195115383
Издательство: Oxford Academ
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Цена: 155760.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text covers areas of geostatistical methodology, including: tools for description; quantitative modelling of spatial continuity; spatial prediction; and theoretical background for GSLIB programmes. Application is to environmental data, but the algorithms may be used in other disciplines.