Models for Ecological Data : : An Introduction / / James S. Clark.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Clark, James S.,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2020]; ©2007
• Year of Publication: 2020
• Language: English
• Physical Description: 1 online resource (632 p.) :; 163 line illus. 21 tables.
• Other title: Frontmatter --; Contents --; Preface --; Part I Introduction --; 1. Models in Context --; 2. Model Elements: Application to Population Growth --; Part II. Elements of Inference --; 3. Point Estimation: Maximum Likelihood and the Method of Moments --; 4. Elements of the Bayesian Approach --; 5. Confidence Envelopes and Prediction Intervals --; 6. Model Assessment and Selection --; Part III. Larger Models --; 7. Computational Bayes: Introduction to Tools Simulation --; 8. A Closer Look at Hierarchical Structures --; Part IV. More Advanced Methods --; 9. Time --; 10. Space-Time --; 11. Some Concluding Perspectives --; References --; INDEX
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9780691220123; 9783110442502
• DOI: 10.1515/9780691220123?locatt=mode:legacy
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: James S. Clark.

The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Accompanying lab manual in R