By: Éric Parent (Author), Étienne Rivot (Author)
405 pages, 100 b/w photos and illustrations, tables
Bayesian statistics are becoming the contemporary standard for treating ecological data. Introduction to Hierarchical Bayesian Modeling of Ecological Data 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.
- The Elementary Beta-Binomial Model for a Capture-Marked Experiment
- The Normal Model: Does the Fish Farm Pollution Influence Juvenile Growth?
- Playing with the Beta-Binomial Model
- Introducing Explanatory Variables
- Borrowing Strength from Similar Units?
- Observations Errors
- More than One Component Data
- Cocktail Models for Combining Various Sources of Information
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