| 书目名称 | Random Fields for Spatial Data Modeling |
| 副标题 | A Primer for Scienti |
| 编辑 | Dionissios T. Hristopulos |
| 视频video | |
| 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 |
| 丛书名称 | Advances in Geographic Information Science |
| 图书封面 |  |
| 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 |
| 关键词 | Conditional Simulation; Gaussian Statistical Field Theory; Local Interaction Models; Random Fields; Spat |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-94-024-1918-4 |
| isbn_ebook | 978-94-024-1918-4Series ISSN 1867-2434 Series E-ISSN 1867-2442 |
| issn_series | 1867-2434 |
| copyright | Springer Nature B.V. 2020 |
发表于 2025-3-21 19:02:39
