Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications.

James S. Clark and Alan E. Gelfand.

ISBN 10: 0198569661 / ISBN 13: 9780198569664

Editore: Oxford University Press 2006, 2006

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First edition. Hardback. Large octavo. ix, 205pp. Illustrated boards. A near fine, unread copy. No jacket as issued.

Codice articolo C27655

Dati bibliografici

• Titolo: Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications.
• Editore: Oxford University Press 2006
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 0198569661
• ISBN 13: 9780198569664
• Rilegatura: Rilegato
• Edizione: prima edizione
• Condizione: Fine

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Jim Clark is the Blomquist professor at Duke University, where his research focuses on how global change affects forests and grasslands. He received a B.S. from the North Carolina State University in Entomology (1979), a M.S. from the University of Massachusetts in Forestry and Wildlife (1984), and a Ph.D. from the University of Minnesota in Ecology (1988). At Duke University, Clark teaches Community Ecology and Ecological Models & Data. He has served as the Director of Graduate Studies for the University Program in Ecology and as Director of the Center on Global Change. Alan E. Gelfand is the J B Duke Professor of Statistics and Decision Sciences at Duke University. An early contributor to the development of computational machinery for fitting hierarchical Bayesian models, his current research focuses on the analysis of spatial and spatio-temporal data. His primary areas of application are to problems in environmental science, ecology, and climatology. He received a B.S. from the City College of New York and an M.S. and Ph.D. from Stanford University. After many years at the University of Connecticut, he joined the faculty at Duke University in August 2002.