Schlagwort(e):
Bayesian statistical decision theory.
;
Multilevel models (Statistics).
;
Mathematical statistics -- Data processing.
;
Environmental sciences -- Statistical methods.
;
Electronic books.
Beschreibung / Inhaltsverzeichnis:
New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes and Markov Chain Monte Carlo methods for analysis provide a consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complicated, and responses depend on scale. Nowhere are these methods more promising than in the environmental sciences.
Materialart:
Online-Ressource
Seiten:
1 online resource (216 pages)
Ausgabe:
1st ed.
ISBN:
9780191513848
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=430458
DDC:
577.01/519542
Sprache:
Englisch
Anmerkung:
Intro -- Contents -- Preface -- Contributors -- Part I: Introduction to hierarchical modeling -- 1 Elements of hierarchical Bayesian inference -- 2 Bayesian hierarchical models in geographical genetics -- Part II: Hierarchical models in experimental settings -- 3 Synthesizing ecological experiments and observational data with hierarchical Bayes -- 4 Effects of global change on inflorescence production: a Bayesian hierarchical analysis -- Part III: Spatial modeling -- 5 Building statistical models to analyze species distributions -- 6 Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis -- Part IV: Spatio-temporal modeling -- 7 Spatial-temporal statistical modeling and prediction of environmental processes -- 8 Hierarchical Bayesian spatio-temporal models for population spread -- 9 Spatial models for the distribution of extremes -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.
