Statistical Computation for Environmental Sciences in R
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A01=James S. Clark
Adaptive management
Author_James S. Clark
Bayes factor
Bayesian
Biodiversity
Biomass (ecology)
Category=PSAF
Climate change mitigation
Climate model
Correlation and dependence
Correlation function
Covariance function
Covariance matrix
Covariate
Cumulative distribution function
Cumulative effects (environment)
Data set
Decision theory
Design matrix
Deviance information criterion
Ecological analysis
Ecological fallacy
Ecological forecasting
Ecological study
Ecological threshold
Ecology
Ecosystem health
Ecosystem model
Empirical distribution function
Environmental science
Environmental statistics
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Equation
Estimation
Estimation theory
Estimator
Fecundity
Fisher information
Generalized linear model
Gibbs sampling
Importance sampling
Inference
Joint probability distribution
Kalman filter
Likelihood function
Likelihood-ratio test
Logistic function
Logistic Models
Logistic regression
Logit
Marginal distribution
Meta-analysis
Model selection
Observational study
Parameter
Point estimation
Poisson sampling
Population ecology
Population model
Prediction
Probability
Random effects model
Ranking (information retrieval)
Sampling distribution
Scale parameter
Sensitivity analysis
SETAR (model)
State variable
Stochastic
Survival analysis
Theoretical ecology
Threshold model
Utility
Product details
- ISBN 9780691122625
- Weight: 369g
- Dimensions: 216 x 279mm
- Publication Date: 03 Jun 2007
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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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
James S. Clark is H. L. Blomquist Professor in the Nicholas School for the Environment, Professor of Statistics and Decisions Sciences, and Professor of Biology at Duke University. An award-winning researcher on the ecological impact of climate change, he is the author of more than one hundred papers for leading journals, including "Science, Nature, and Ecology". In 2005, he was elected to the American Academy of Arts and Sciences.
