大葉大學圖書館 |

Theoretical aspects of spatial-temporal modeling[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5
書名/作者:	Theoretical aspects of spatial-temporal modeling/ edited by Gareth William Peters, Tomoko Matsui.
其他作者:	Peters, Gareth William.
出版者:	Tokyo : : Springer Japan :, 2015.
面頁冊數:	xv, 124 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Mathematical statistics.
標題:	Spatial analysis (Statistics)
標題:	Time-series analysis.
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Statistics and Computing/Statistics Programs.
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
ISBN:	9784431553366
ISBN:	9784431553359
摘要、提要註:	This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters) The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.
電子資源:	http://dx.doi.org/10.1007/978-4-431-55336-6

Theoretical aspects of spatial-temporal modeling[electronic resource] /
Theoretical aspects of spatial-temporal modeling[electronic resource] /edited by Gareth William Peters, Tomoko Matsui. - Tokyo :Springer Japan :2015. - xv, 124 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters) The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

ISBN: 9784431553366

Standard No.: 10.1007/978-4-431-55336-6doiSubjects--Topical Terms:

171875
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

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