Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010338215 | QH541.15.S72 P37 2013 | Open Access Book | Book | Searching... |
On Order
Summary
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.
Author Notes
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.
Table of Contents
| I Basic Blocks of Bayesian Modeling |
| Bayesian Hierarchical Models in Statistical Ecology |
| Challenges for statistical ecology |
| Conditional reasoning, graphs and hierarchical models |
| Bayesian inferences on hierarchical models |
| What can be found in this book? |
| The Beta-Binomial Model |
| From a scientific question to a Bayesian analysis |
| What is modeling? |
| Think conditionally and make a graphical representation |
| Inference is the reverse way of thinking |
| Expertise matters |
| Encoding prior knowledge |
| The conjugate Beta pdf |
| Bayesian inference as statistical learning |
| Bayesian inference as a statistical tool for prediction |
| Asymptotic behavior of the beta-binomial model |
| The beta-binomial model with WinBUGS |
| Further references |
| The Basic Normal Model |
| Salmon farm's pollutants and juvenile growth |
| A Normal model for the fish length |
| Normal-gamma as conjugate models to encode expertise |
| Inference by recourse to conjugate property |
| Bibliographical notes |
| Further material |
| Working with More Than One Beta-Binomial Element |
| Capture-mark-recapture analysis |
| Successive removal analysis |
| Testing a new tag for tuna |
| Further references |
| Combining Various Sources of Information |
| Motivating example |
| Stochastic model for salmon behavior |
| Inference with WinBUGS |
| Results |
| Discussion and conclusions |
| The Normal Linear Model |
| The decrease of Thiof abundance in Senegal |
| Linear model theory |
| A linear model for Thiof abundance |
| Further reading |
| Nonlinear Models for Stock-Recruitment Analysis |
| Stock-recruitment motivating example |
| Searching for a SR model |
| Which parameters? |
| Changing the error term from lognormal to gamma |
| From Ricker to Beverton and Holt |
| Model choice with informative prior |
| Conclusions and perspectives |
| Getting beyond Regression Models |
| Logistic and probit regressions |
| Ordered probit model |
| Discussion |
| II More Elaborate Hierarchical Structures |
| HBM I : Borrowing Strength from Similar Units |
| Introduction |
| HBM for capture-mark-recapture data |
| Hierarchical stock-recruitment analysis |
| Further Bayesian comments on exchangeability |
| HBM II : Piling up Simple Layers |
| HBM for successive removal data with habitat and year |
| Electrofishing with successive removals |
| HBM III : State-Space Modeling |
| Introduction |
| State-space modeling of a biomass production model |
| State-space modeling of Atlantic salmon life cycle model |
| A tool of choice for the ecological detective |
| Decision and Planning |
| Summary |
| Introduction |
| The Sée-Sélune river network |
| Salmon life cycle dynamics |
| Long-term behavior: Collapse or equilibrium? |
| Management reference points |
| Management rules and implementation error |
| Economic model |
| Results |
| Discussion |
| Appendix A The Normal and Linear Normal Model |
| Appendix B Computing Marginal Likelihoods |
| Appendix C The Baseball Players' Historical Example |
| Appendix D More on Ricker Stock-Recruitment |
| Bibliography |
| Index |
