| Titre : |
Hierarchical modelling for the environmental sciences: statistical methods and applications |
| Type de document : |
livre |
| Auteurs : |
Clark, J.S.(Ed.) ; Gelfand, A.E.(Ed.) |
| Mention d'édition : |
1st ed. |
| Editeur : |
Oxford, UK : Oxford University Press |
| Année de publication : |
2006 |
| Importance : |
205 p. |
| ISBN/ISSN/EAN : |
978-0-19-856967-1 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Environmental modelling Hierarchical model Spatial modeling Statistical methods |
| Résumé : |
New Statistical tools are changing the wau in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide constant framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirment for a clear exposition of the methodology through to application for a range of environmental challenges. [Résumé éditeur] |
Hierarchical modelling for the environmental sciences: statistical methods and applications [livre] / Clark, J.S.(Ed.) ; Gelfand, A.E.(Ed.) . - 1st ed. . - Oxford, UK : Oxford University Press, 2006 . - 205 p. ISBN : 978-0-19-856967-1 Langues : Anglais ( eng)
| Mots-clés : |
Environmental modelling Hierarchical model Spatial modeling Statistical methods |
| Résumé : |
New Statistical tools are changing the wau in which scientists analyze and interpret data and models. Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. In particular, hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide constant framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences. Models have developed rapidly, and there is now a requirment for a clear exposition of the methodology through to application for a range of environmental challenges. [Résumé éditeur] |
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