Clark J.S., Gelfand A.E. (eds.) Hierarchical Modelling for the Environmental Sciences. Statistical Methods and Applications

Oxford University Press, 2006. — 216 p.New statistical tools are changing the way in which scientists analyze and interpret data and models. Hierarchical Bayes modeling with sampling based methods for fitting and 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. These methods have developed rapidly, and there is demand for applications to a range of environmental problems. This book makes use of specific applications to provide a nontechnical overview of hierarchical Bayes and Markov chain Monte Carlo Methods.Part I Introduction to hierarchical modeling.
Elements of hierarchical Bayesian inference (Bradley P. Carlin, James S. Clark, and Alan E. Gelfand).
Bayesian hierarchical models in geographical genetics (Kent E. Holsinger).
Part II Hierarchical models in experimental settings.
Synthesizing ecological experiments and observational data with hierarchical Bayes (James S. Clark and Shannon LaDeau).
Effects of global change on inflorescence production: a Bayesian hierarchical analysis (Janneke Hille Ris Lambers, Brian Aukema, Jeff Diez, Margaret Evans, and Andrew Latimer).
Part III Spatial modeling.
Building statistical models to analyze species distributions (Alan E. Gelfand, Andrew Latimer, Shanshan Wu, and John A Silander, Jr).
Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis (Kiona Ogle, Maria Uriarte, Jill Thompson, Jill Johnstone, Andy Jones, Yiching Lin, Eliot J. B. McIntire, and Jess K. Zimmerman).
Part IV Spatio-temporal modeling.
Spatial–temporal statistical modeling and prediction of environmental processes (Li Chen, Montserrat Fuentes, and Jerry M. Davis).
Hierarchical Bayesian spatio–temporal models for population spread (Christopher K. Wikle and Mevin B. Hooten).
Spatial models for the distribution of extremes (Eric Gilleland, Douglas Nychka and Uli Schneider).