Making statistical modeling and inference more accessible to ecologists and related scientists, this book gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competi
Making statistical modeling and inference more accessible to ecologists and related scientists, this book gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competi
Éric Parent is head of the Research Laboratory for Risk Management in Environmental Sciences (Team MORSE) and a professor in applied statistics and probabilistic modeling for environmental engineering at the National Institute for Rural Engineering, Water and Forest Management (ENGREF/AgroParisTech) in Paris, France. Dr. Parent's research encompasses Bayesian theory and applications, with special emphasis on environmental systems modeling. Étienne Rivot is a researcher in the Fisheries Ecology Laboratory at Agrocampus Ouest in Rennes, France. Dr. Rivot's research focuses on the application of Bayesian statistical modeling for the analysis of ecological data, inference, and predictions.
Inhaltsangabe
I Basic Blocks of Bayesian Modeling: Bayesian Hierarchical Models in Statistical Ecology. The Beta-Binomial Model. The Basic Normal Model. Working with More Than One Beta-Binomial Element. Combining Various Sources of Information. The Normal Linear Model. Nonlinear Models for Stock-Recruitment Analysis. Getting beyond Regression Models. II More Elaborate Hierarchical Structures: HBM I: Borrowing Strength from Similar Units. HBM II: Piling up Simple Layers. HBM III: State-Space Modeling. Decision and Planning. Appendices. Bibliography. Index.
I Basic Blocks of Bayesian Modeling: Bayesian Hierarchical Models in Statistical Ecology. The Beta-Binomial Model. The Basic Normal Model. Working with More Than One Beta-Binomial Element. Combining Various Sources of Information. The Normal Linear Model. Nonlinear Models for Stock-Recruitment Analysis. Getting beyond Regression Models. II More Elaborate Hierarchical Structures: HBM I: Borrowing Strength from Similar Units. HBM II: Piling up Simple Layers. HBM III: State-Space Modeling. Decision and Planning. Appendices. Bibliography. Index.
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