Hierarchical Modelling for the Environmental Sciences
Statistical methods and applications
Author: Clark and Gelfand Series: Oxford Biology (Paperback)
Preface Part I. Introduction to hierarchical modeling 1. Bradley P. Carlin, James S. Clark and Alan E. Gelfand: Elements of hierarchical Bayesian influence 2. Kent Holsinger: Bayesian hierarchical models in geographical genetics Part II. Hierarchical models in experimental settings 3. James S. Clark and Shannon LaDeau: Synthesizing ecological experiments and observational data with hierarchical Bayes 4. Janneke Hille Ris Lambers, Brian Aukean, Jeff Diez, Margaret Evans and Andrew Latimer: Effects of global change on inflorescence production: a Bayesian hierarchical analysis Part III. Spatial modeling 5. Alan E. Gelfand, Andrew Latimer, Shanshan Wu and John A. Silander, Jr.: Building statistical models to analyse species distributions 6. Kiona Ogle, Maria Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin, Eliot J. B. McIntire and Jess K. Zimmmerman: Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis Part IV. Spatio-temporal modeling 7. Li Chen, Montserrat Fuentes and Jerry M. Davis: Spatial temporal statistical modeling and prediction of environmental processes 8. Christopher K. Wikle and Melvin B. Hooten: Hierarchical Bayesian spatio-temporal models for population spread 9. Eric Gilleland, Douglas Nychka and Uli Schneider: Spatial models for the distribution of extremes References Index
Statistical tools are changing the ways in which scientists analyze and interpret data and models. This book provides consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale.
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Summary
Preface Part I. Introduction to hierarchical modeling 1. Bradley P. Carlin, James S. Clark and Alan E. Gelfand: Elements of hierarchical Bayesian influence 2. Kent Holsinger: Bayesian hierarchical models in geographical genetics Part II. Hierarchical models in experimental settings 3. James S. Clark and Shannon LaDeau: Synthesizing ecological experiments and observational data with hierarchical Bayes 4. Janneke Hille Ris Lambers, Brian Aukean, Jeff Diez, Margaret Evans and Andrew Latimer: Effects of global change on inflorescence production: a Bayesian hierarchical analysis Part III. Spatial modeling 5. Alan E. Gelfand, Andrew Latimer, Shanshan Wu and John A. Silander, Jr.: Building statistical models to analyse species distributions 6. Kiona Ogle, Maria Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin, Eliot J. B. McIntire and Jess K. Zimmmerman: Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis Part IV. Spatio-temporal modeling 7. Li Chen, Montserrat Fuentes and Jerry M. Davis: Spatial temporal statistical modeling and prediction of environmental processes 8. Christopher K. Wikle and Melvin B. Hooten: Hierarchical Bayesian spatio-temporal models for population spread 9. Eric Gilleland, Douglas Nychka and Uli Schneider: Spatial models for the distribution of extremes References Index
Statistical tools are changing the ways in which scientists analyze and interpret data and models. This book provides consistent framework for inference and prediction where information is heterogeneous and uncertain, processes are complex, and responses depend on scale.
Description
New statistical tools are changing the ways 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 consistent 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 requirement for a clear exposition of the methodology through to applicationfor a range of environmental challenges.
Critic Reviews
“"...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." --Bulletin of the British Ecological Society "This book provides rare examples of how to apply HB in the environmental sciences and will be a key component of the analytical toolbox for environmental scientists using or learning to use this approach." --The Quarterly Review of Biology”
'...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 38:1
About the Author
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 asthe 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 DukeUniversity. 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 DukeUniversity in August 2002.
New statistical tools are changing the ways 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 consistent 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 requirement for a clear exposition of the methodology through to application for a range of environmental challenges.
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