205 pages, 73 line figures
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.
* Jointly edited by a leading ecologist and statistician, with contributions from recognized experts in the field
* Introduces environmental scientists to modern statistical computation techniques
* Provides a non-technical overview of hierarchical Bayes and Markov Chain Monte Carlo methods for analysis of environmental data
* Includes chapters demonstrating the application of methods to a range of environmental challenges
The book is targeted primarily at graduate level students in the environmental sciences, particularly ecology. It will also be useful to professional researchers at the interfaces of biology, mathematics and statistics.
'...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
Preface; PART I. INTRODUCTION TO HIERARCHICAL MODELING; 1. Elements of hierarchical Bayesian influence; 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 analyse 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
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