國立虎尾科技大學 |

Theoretical aspects of spatial-temporal modeling

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Theoretical aspects of spatial-temporal modeling/ edited 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

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

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

527941
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Theoretical aspects of spatial-temporal modeling
LDR:02514nam a2200313 a 4500 001 839023
003 DE-He213
005 20160513093802.0
006 m d
007 cr nn 008maaau
008 160616s2015 ja s 0 eng d
020 $a 9784431553366 $q (electronic bk.)
020 $a 9784431553359 $q (paper)
024 7 $a 10.1007/978-4-431-55336-6 $2 doi
035 $a 978-4-431-55336-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .T396 2015
245 0 0 $a Theoretical aspects of spatial-temporal modeling $h [electronic resource] / $c edited by Gareth William Peters, Tomoko Matsui.
260 $a Tokyo : $c 2015. $b Springer Japan : $b Imprint: Springer,
300 $a xv, 124 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-544X
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.
650 0 $a Mathematical statistics. $3 527941
650 0 $a Spatial analysis (Statistics) $3 562936
650 0 $a Time-series analysis. $3 528412
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
700 1 $a Peters, Gareth William. $3 1070524
700 1 $a Matsui, Tomoko. $3 1070525
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in statistics. $3 884250
856 4 0 $u http://dx.doi.org/10.1007/978-4-431-55336-6
950 $a Mathematics and Statistics (Springer-11649)