Leseprobe
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Models for Ecological Data
James S. Clark
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Naturwissenschaften, Medizin, Informatik, Technik / Naturwissenschaften allgemein
Beschreibung
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
- Lab manual in R is available separately
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Utility, Gibbs sampling, Covariance function, Logit, Covariance matrix, Model selection, Fisher information, Estimation, Correlation function, Joint probability distribution, Environmental science, Decision theory, Logistic function, Marginal distribution, Importance sampling, Probability, Sampling distribution, Ecosystem health, Prediction, Ecological fallacy, Ecological threshold, Environmental statistics, Biomass (ecology), Sensitivity analysis, Likelihood function, Bayesian, Ecological forecasting, Ecological analysis, Point estimation, Ecology, Random effects model, Adaptive management, Population ecology, Ecosystem model, Fecundity, Ranking (information retrieval), Bayes factor, Correlation and dependence, Theoretical ecology, Ecological study, Logistic regression, Cumulative effects (environment), Survival analysis, Observational study, Population model, Stochastic, Deviance information criterion, Logistic Models, State variable, Inference, Covariate, Parameter, Climate change mitigation, Scale parameter, SETAR (model), Cumulative distribution function, Estimator, Likelihood-ratio test, Kalman filter, Meta-analysis, Threshold model, Generalized linear model, Biodiversity, Climate model, Data set, Design matrix, Empirical distribution function, Equation, Estimation theory, Poisson sampling