Introduction to Hierarchical Bayesian Modeling for Ecological Data

✍ Scribed by E. Parent, E. Rivot

• Publisher: Taylor & Francis
• Year: 2012
• Tongue: English
• Leaves: 426
• Series: Applied environmental statistics
• Category: Library

✦ Synopsis

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Datagives 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.

✦ Table of Contents

Contents......Page 6
List of Figures......Page 14
List of Tables......Page 18
Foreword......Page 20
I. Basic blocks of Bayesian modeling......Page 24
1. Bayesian hierarchical models in statistical ecology......Page 26
2. The Beta-Binomial model......Page 66
3. The basic Normal model......Page 88
4. Working with more than one Beta-Binomial element......Page 106
5. Combining various sources of information......Page 128
6. The Normal linear model......Page 148
7. Nonlinear models for stock-recruitment analysis......Page 168
8. Getting beyond regression models......Page 192
II. More elaborate hierarchical structures......Page 216
9. HBM I: Borrowing strength from similar units......Page 218
10. HBM II: Piling up simple layers......Page 244
11. HBM III: State-space modeling......Page 280
12. Decision and planning......Page 324
A. The Normal and Linear Normal model......Page 356
B. Computing marginal likelihoods and DIC......Page 366
C. More on Ricker stock-recruitment......Page 370
D. Some predictive and conditional pdfs......Page 382
E. The baseball players' historical example......Page 386
Bibliography......Page 398