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E-BOOK
Title Models for Ecological Data : An Introduction / James S. Clark.
Imprint Princeton, NJ : Princeton University Press, [2020]
©2007.

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 Internet  Electronic Book    AVAILABLE
Description 1 online resource (632 p.) : 163 line illus. 21 tables
Note Available only to authorized UTEP users.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Okt 2020).
Subject Ecology -- Mathematical models.
Environmental sciences -- Mathematical models.
Contents Frontmatter -- Contents -- Preface -- 1. Models in Context -- 2. Model Elements: Application to Population Growth -- 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 -- 7. Computational Bayes: Introduction to Tools Simulation -- 8. A Closer Look at Hierarchical Structures -- 9. Time -- 10. Space-Time -- 11. Some Concluding Perspectives -- References -- INDEX.
Summary 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.