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Titlebook: Random Fields for Spatial Data Modeling; A Primer for Scienti Dionissios T. Hristopulos Textbook 2020 Springer Nature B.V. 2020 Conditional

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發(fā)表于 2025-3-21 19:02:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Random Fields for Spatial Data Modeling
副標(biāo)題A Primer for Scienti
編輯Dionissios T. Hristopulos
視頻videohttp://file.papertrans.cn/822/821046/821046.mp4
概述Provides a bridge between statistical physics and spatial statistics and underlines links between geostatistics, applied mathematics and machine learning.Presents a unique approach, developed by the a
叢書名稱Advances in Geographic Information Science
圖書封面Titlebook: Random Fields for Spatial Data Modeling; A Primer for Scienti Dionissios T. Hristopulos Textbook 2020 Springer Nature B.V. 2020 Conditional
描述This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis.?.The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and variational methods).? The book also explores links between random fields, Gaussian processes?and neural networks?used in machine learning. Connections with applied mathematics are highlighted by means ofmodels based on stochastic partial differential equations. An interlude on autoregressive time series provides useful lower-dimensional analogies?and a connection with the classical linear harmonic oscillator. Other chapters focus on non-Gaussian rand
出版日期Textbook 2020
關(guān)鍵詞Conditional Simulation; Gaussian Statistical Field Theory; Local Interaction Models; Random Fields; Spat
版次1
doihttps://doi.org/10.1007/978-94-024-1918-4
isbn_ebook978-94-024-1918-4Series ISSN 1867-2434 Series E-ISSN 1867-2442
issn_series 1867-2434
copyrightSpringer Nature B.V. 2020
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沙發(fā)
發(fā)表于 2025-3-21 21:12:43 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:02:33 | 只看該作者
Additional Topics of Random Field Modeling, types of anisotropy, and the description of the joint dependence of random fields at more than two points. Ergodicity, isotropy and anisotropy are properties that have significant practical interest for the modeling of spatial data. On the other hand, the joint .-point dependence is a more advanced
地板
發(fā)表于 2025-3-22 08:31:42 | 只看該作者
Geometric Properties of Random Fields,ian random functions is to a large extent determined by the mean and the two-point correlation functions. The classical text on the geometry of random fields is the book written by Robert Adler [.]. The basic elements of random field geometry are contained in the technical report by Abrahamsen [.].
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發(fā)表于 2025-3-22 13:45:31 | 只看該作者
Random Fields Based on Local Interactions,ective is useful, because it can lead to computationally efficient methods for spatial prediction, while it is also related with Markovian random fields. In addition, it enables the calculation of new forms of covariance functions and provides a link with ..
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發(fā)表于 2025-3-22 20:31:46 | 只看該作者
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發(fā)表于 2025-3-22 21:46:14 | 只看該作者
Spatial Prediction Fundamentals,ble spatial model and the “best” values for the parameters of the model. Parameter estimation is not necessary for certain simple deterministic models (e.g., nearest neighbor method), since such models do not involve any free parameters. . is then used to choose the “optimal model” (based on some sp
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發(fā)表于 2025-3-23 02:56:57 | 只看該作者
More on Spatial Prediction,h generalizations include the application of ordinary kriging to . that can handle non-stationary data, as well as the methods of . and . that incorporate deterministic trends in the linear prediction equation [.]. . allows combining multivariate information in the prediction equations. Various . of
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發(fā)表于 2025-3-23 06:25:44 | 只看該作者
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