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＜電子ブック＞
Theoretical Aspects of Spatial-Temporal Modeling / edited by Gareth William Peters, Tomoko Matsui
(JSS Research Series in Statistics. ISSN:23640065)

版	1st ed. 2015.
出版者	(Tokyo : Springer Japan : Imprint: Springer)
出版年	2015
本文言語	英語
大きさ	XV, 124 p. 18 illus., 13 illus. in color : online resource
著者標目	Peters, Gareth William editor Matsui, Tomoko editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Mathematical statistics -- Data processing  全ての件名で検索 FREE:Statistical Theory and Methods FREE:Statistics and Computing FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	1  Particle association measures and multiple target tracking(Pierre Del Moral and Jeremie Houssineau) -- 2 An Overview of Recent Advances in Monte-Carlo Methods for Bayesian Filtering in High-dimensional Spaces (François Septier and Gareth W. Peters) -- 3 Spectral Measures of α-stable Distributions: An overview and natural applications in Wireless Communications (Nourddine Azzaoui, Laurent Clavier, Arnaud Guillin and Gareth W. Peters) -- 4 Networks, Random Graphs and Percolation (Philippe Deprez and Mario V. Wüthrich) 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 geometryfeatures HTTP:URL=https://doi.org/10.1007/978-4-431-55336-6

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