Multiscale Modelling : A Bayesian Perspective
A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement. This book contains methodology for the analysis of data that arise from such multiscale processes. The book brings together a number of recent developments and makes them accessible to a wider...
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| Auteurs principaux: | , |
|---|---|
| 格式: | Livre numérique |
| 語言: | Anglais |
| 出版: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2007. |
| 叢編: | Springer Series in Statistics
|
| 主題: | |
| 在線閱讀: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 提示: |
L'impression du document génère 239 p. Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Multiscale modeling, a Bayesian perspective, Marco A.R. Ferreira, Herbert K.H. Lee, 2007, New York, Springer, 1 volume (XII-245 pages), Springer series in statistics, 978-0-387-70897-3 |
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| 100 | 1 | |a Ferreira, Marco Antonio Rosa, |d 1969- | |
| 245 | 1 | 0 | |a Multiscale Modelling : |b A Bayesian Perspective |c Marco A.R. Ferreira, Herbert K.H. Lee. |
| 250 | |a 1st ed. 2007. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Series in Statistics |x 2197-568X | |
| 500 | |a L'impression du document génère 239 p. | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 504 | |a Bibliogr. index | ||
| 505 | 1 | |a Models for Spatial Data Illustrative Example Convolutions and Wavelets Convolution Methods Wavelet Methods Explicit Multiscale Models Overview of Explicit Multiscale Models Gaussian Multiscale Models on Trees Hidden Markov Models on Trees Mass-Balanced Multiscale Models on Trees Multiscale Random Fields Multiscale Time Series Change of Support Models Implicit Multiscale Models Implicit Computationally Linked Model Overview Metropolis-Coupled Methods Genetic Algorithms Case Studies Soil Permeability Estimation Single Photon Emission Computed Tomography Example Conclusions. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement. This book contains methodology for the analysis of data that arise from such multiscale processes. The book brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. The Bayesian approach also facilitates the use of knowledge from prior experience or data, and these methods can handle different amounts of prior knowledge at different scales, as often occurs in practice. The book is aimed at statisticians, applied mathematicians, and engineers working on problems dealing with multiscale processes in time and/or space, such as in engineering, finance, and environmetrics. The book will also be of interest to those working on multiscale computation research. The main prerequisites are knowledge of Bayesian statistics and basic Markov chain Monte Carlo methods. A number of real-world examples are thoroughly analyzed in order to demonstrate the methods and to assist the readers in applying these methods to their own work. To further assist readers, the authors are making source code (for R) available for many of the basic methods discussed herein. Marco A. R. Ferreira is an Assistant Professor of Statistics at the University of Missouri, Columbia. Herbert K. H. Lee is an Associate Professor of Applied Mathematics and Statistics at the University of California, Santa Cruz, and authored the book Bayesian Nonparametrics via Neural Networks. | ||
| 650 | |a Analyse multiéchelle | ||
| 650 | |a Statistique bayésienne | ||
| 650 | |a Série chronologique | ||
| 650 | |a Convolutions (mathématiques) | ||
| 650 | |a Ondelettes | ||
| 700 | 1 | |a Lee, Herbert K. H. |4 aut | |
| 776 | 0 | |0 119125390 |t Multiscale modeling |o a Bayesian perspective |f Marco A.R. Ferreira, Herbert K.H. Lee |d 2007 |c New York |n Springer |p 1 volume (XII-245 pages) |s Springer series in statistics |z 978-0-387-70897-3 | |
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