Hierarchical modelling for the environmental sciences : statistical methods and applications
著者
書誌事項
Hierarchical modelling for the environmental sciences : statistical methods and applications
(Oxford biology)
Oxford University Press, c2006
- :hbk
- :pbk
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注記
Includes bibliographical references
内容説明・目次
- 巻冊次
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:hbk ISBN 9780198569664
目次
- 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
- 巻冊次
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:pbk ISBN 9780198569671
内容説明
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.
目次
- PART I. INTRODUCTION TO HIERARCHICAL MODELING
- PART II. HIERARCHICAL MODELS IN EXPERIMENTAL SETTINGS
- PART III. SPATIAL MODELING
- PART IV. SPATIO-TEMPORAL MODELING
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