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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More on Estimation, the . is relatively new, and its statistical properties have not been fully explored. The method of . was used by Edwin Thompson Jaynes to derive statistical mechanics based on information theory [406, 407]. Following the work of Jaynes, maximum entropy has found several applications in physics [67

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Beyond the Gaussian Models,atial data often exhibit properties such as (i) strictly positive values (ii) asymmetric (skewed) probability distributions (iii) long positive tails (e.g., power-law decay of the pdf) and (iv) compact support.

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1867-2434 hine learning.Presents a unique approach, developed by the aThis 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 g

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Gaussian Random Fields,thematical simplifications that they enable, such as the decomposition of many-point correlation functions into products of two-point correlation functions. The simplifications achieved by Gaussian random fields are based on fact that the joint Gaussian probability density function is fully determined by the mean and the covariance function.

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Spatial Prediction Fundamentals, (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 specified statistical criterion) among a suite of candidates.