Introduction to Hierarchical Bayesian Modeling for Ecological Data

Eric Parent, Etienne Rivot

© 2012 - Chapman and Hall/CRC
Published August 21, 2012
Reference - 427 Pages - 143 B/W Illustrations
ISBN 9781584889199 - CAT# C9195
Series: Chapman & Hall/CRC Applied Environmental Statistics

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Features

  • Explains how to design models for analyzing ecological data
  • Illustrates how the hierarchical Bayesian modeling framework can overcome difficulties associated with classical statistical modeling toolboxes
  • Uses real data drawn from fish population studies
  • Includes many data sets, exercises, and R and WinBUGS codes on the authors’ website www.hbm-for-ecology.org

Summary

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models.

The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website.

This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

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