Models for Ecological Data : : An Introduction / / 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...
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Place / Publishing House: | Princeton, NJ : : Princeton University Press, , [2020] ©2007 |
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Clark, James S., author. aut http://id.loc.gov/vocabulary/relators/aut Models for Ecological Data : An Introduction / James S. Clark. Princeton, NJ : Princeton University Press, [2020] ©2007 1 online resource (632 p.) : 163 line illus. 21 tables. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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 restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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 Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) Ecology Mathematical models. Environmental sciences Mathematical models. SCIENCE / Environmental Science (see also Chemistry / Environmental). bisacsh Dirichlet distribution. Fisher Information. Hadamard product. Poisson. Weibull distribution. autocorrelation. autocovariance. beta distribution. beta-binomial. binomial distribution. completing the square. confidence interval. correlation. covariance. differential equation. eigenanalysis. exponential distribution. extreme value distribution. fecundity. frequentist. gamma distribution. generation time. integrated analysis. inverse gamma. kriging. logistic population growth. longitudinal model. multinomial. negative binomial. positive definite matrix. predictive loss. spectral density. stage structured model. uniform distribution. Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 9783110442502 https://doi.org/10.1515/9780691220123?locatt=mode:legacy https://www.degruyter.com/isbn/9780691220123 Cover https://www.degruyter.com/cover/covers/9780691220123.jpg |
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English |
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Clark, James S., Clark, James S., |
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Clark, James S., Clark, James S., Models for Ecological Data : An Introduction / 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 |
author_facet |
Clark, James S., Clark, James S., |
author_variant |
j s c js jsc j s c js jsc |
author_role |
VerfasserIn VerfasserIn |
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Clark, James S., |
title |
Models for Ecological Data : An Introduction / |
title_sub |
An Introduction / |
title_full |
Models for Ecological Data : An Introduction / James S. Clark. |
title_fullStr |
Models for Ecological Data : An Introduction / James S. Clark. |
title_full_unstemmed |
Models for Ecological Data : An Introduction / James S. Clark. |
title_auth |
Models for Ecological Data : An Introduction / |
title_alt |
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 |
title_new |
Models for Ecological Data : |
title_sort |
models for ecological data : an introduction / |
publisher |
Princeton University Press, |
publishDate |
2020 |
physical |
1 online resource (632 p.) : 163 line illus. 21 tables. |
contents |
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 |
isbn |
9780691220123 9783110442502 |
callnumber-first |
Q - Science |
callnumber-subject |
QH - Natural History and Biology |
callnumber-label |
QH541 |
callnumber-sort |
QH 3541.15 M3 C53 42007 |
url |
https://doi.org/10.1515/9780691220123?locatt=mode:legacy https://www.degruyter.com/isbn/9780691220123 https://www.degruyter.com/cover/covers/9780691220123.jpg |
illustrated |
Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
570 - Life sciences; biology |
dewey-ones |
577 - Ecology |
dewey-full |
577.01/5118 |
dewey-sort |
3577.01 45118 |
dewey-raw |
577.01/5118 |
dewey-search |
577.01/5118 |
doi_str_mv |
10.1515/9780691220123?locatt=mode:legacy |
oclc_num |
1202623650 |
work_keys_str_mv |
AT clarkjamess modelsforecologicaldataanintroduction |
status_str |
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ids_txt_mv |
(DE-B1597)571603 (OCoLC)1202623650 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
is_hierarchy_title |
Models for Ecological Data : An Introduction / |
container_title |
Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013 |
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