
Hierarchical Modelling for the Environmental Sciences
Statistical methods and applications
Oxford University Press
Published on 4. May 2006
Book
Paperback/Softback
216 pages
978-0-19-856967-1 (ISBN)
Description
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.
Reviews / Votes
'...if you are already quite well acquainted with Bayesian concepts and terminology then this book should provide an excellent guide to the application of these advanced statistical techniques within ecology.' Justin Travis, Bulletin of the British Ecological Society 2007 38:1More details
Language
English
Place of publication
Oxford
United Kingdom
Target group
Professional and scholarly
Illustrations
73 line drawings, tables
Dimensions
Height: 246 mm
Width: 189 mm
Thickness: 12 mm
Weight
428 gr
ISBN-13
978-0-19-856967-1 (9780198569671)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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.
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.
Editor
, Nicholas School of the Environment, Duke University, USA
, Institute of Statistics and Decision Sciences, Duke University, USA
Content
PART I. INTRODUCTION TO HIERARCHICAL MODELING; PART II. HIERARCHICAL MODELS IN EXPERIMENTAL SETTINGS; PART III. SPATIAL MODELING; PART IV. SPATIO-TEMPORAL MODELING