Eric Parent, Etienne Rivot

Introduction to Hierarchical Bayesian Modeling for Ecological Data

• Gebundenes Buch

Produktbeschreibung

Bayesian statistics are becoming the contemporary standard for treating ecological data. This book is designed for readers who are interested in the quantitative analysis of environmental data yet reluctant to apply ready-made technical recipes without understanding how and why they work. It focuses on up-to-date ecological issues, including biodiversity, community behavior, and genomics, and shows how they could be revisited by using Bayesian modeling techniques. Highly practical, the text encourages readers to deal with advanced ecological issues in practice and to implement models of their own.

Produktdetails

• Verlag: CRC Press
• Seitenzahl: 432
• Erscheinungstermin: 27. August 2012
• Englisch
• Abmessung: 241mm x 161mm x 27mm
• Gewicht: 711g
• ISBN-13: 9781584889199
• ISBN-10: 1584889195
• Artikelnr.: 22753199

Autorenporträt

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.