東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Random fields for spatial data model...

Hristopulos, Dionissios T.

FindBook

Google Book

Amazon

博客來

Random fields for spatial data modeling = a primer for scientists and engineers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Random fields for spatial data modeling/ by Dionissios T. Hristopulos.
其他題名:	a primer for scientists and engineers /
作者:	Hristopulos, Dionissios T.
出版者:	Dordrecht :Springer Netherlands : : 2020.,
面頁冊數:	xxx, 867 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Random fields. -
電子資源:	https://doi.org/10.1007/978-94-024-1918-4
ISBN:	9789402419184

Random fields for spatial data modeling = a primer for scientists and engineers /
Hristopulos, Dionissios T.

Random fields for spatial data modelinga primer for scientists and engineers /[electronic resource] :by Dionissios T. Hristopulos. - Dordrecht :Springer Netherlands :2020. - xxx, 867 p. :ill., digital ;24 cm. - Advances in geographic information science,1867-2434. - Advances in geographic information science..

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 of models 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 random fields and stochastic simulation methods. The book also presents results based on the author's research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.

ISBN: 9789402419184

Standard No.: 10.1007/978-94-024-1918-4doiSubjects--Topical Terms:

648193
Random fields.

LC Class. No.: QA274.45 / .H757 2020

Dewey Class. No.: 519.23

Random fields for spatial data modeling = a primer for scientists and engineers /
LDR:03227nmm a2200337 a 4500 001 2216165
003 DE-He213
005 20200709104315.0
006 m d
007 cr nn 008maaau
008 201120s2020 ne s 0 eng d
020 $a 9789402419184 $q (electronic bk.)
020 $a 9789402419160 $q (paper)
024 7 $a 10.1007/978-94-024-1918-4 $2 doi
035 $a 978-94-024-1918-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA274.45 $b .H757 2020
072 7 $a JHBC $2 bicssc
072 7 $a SCI064000 $2 bisacsh
072 7 $a JHBC $2 thema
072 7 $a PSAF $2 thema
082 0 4 $a 519.23 $2 23
090 $a QA274.45 $b .H873 2020
100 1 $a Hristopulos, Dionissios T. $3 731137
245 1 0 $a Random fields for spatial data modeling $h [electronic resource] : $b a primer for scientists and engineers / $c by Dionissios T. Hristopulos.
260 $a Dordrecht : $b Springer Netherlands : $b Imprint: Springer, $c 2020.
300 $a xxx, 867 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in geographic information science, $x 1867-2434
520 $a 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 of models 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 random fields and stochastic simulation methods. The book also presents results based on the author's research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.
650 0 $a Random fields. $3 648193
650 0 $a Spatial analysis (Statistics) $3 520421
650 1 4 $a Data-driven Science, Modeling and Theory Building. $3 2210495
650 2 4 $a Geophysics/Geodesy. $3 891016
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Geophysics and Environmental Physics. $3 1530865
650 2 4 $a Mathematical and Computational Engineering. $3 3226306
650 2 4 $a Statistical Physics and Dynamical Systems. $3 3135115
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Advances in geographic information science. $3 1567614
856 4 0 $u https://doi.org/10.1007/978-94-024-1918-4
950 $a Earth and Environmental Science (Springer-11646)