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Theoretical aspects of spatial-tempo...

Matsui, Tomoko.

Theoretical aspects of spatial-temporal modeling

Record Type:	Electronic resources : Monograph/item
Title/Author:	Theoretical aspects of spatial-temporal modelingedited by Gareth William Peters, Tomoko Matsui.
other author:	Peters, Gareth William.
Published:	Tokyo :Springer Japan :2015.
Description:	xv, 124 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical statistics.
Online resource:	http://dx.doi.org/10.1007/978-4-431-55336-6
ISBN:	9784431553366$q(electronic bk.)

Theoretical aspects of spatial-temporal modeling
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$q(electronic bk.)

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

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Theoretical aspects of spatial-temporal modeling
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245 0 0 $a Theoretical aspects of spatial-temporal modeling $h [electronic resource] / $c edited by Gareth William Peters, Tomoko Matsui.
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300 $a xv, 124 p. : $b ill. (some col.), digital ; $c 24 cm.
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520 $a 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.
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