Front Cover -- Introduction to WinBUGS for Ecologists -- Copyright -- A Creed for Modeling -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1. Introduction -- 1.1 Advantages of the Bayesian Approach to Statistics -- 1.2 So Why Then Isn't Everyone a Bayesian? -- 1.3 WinBUGS -- 1.4 Why This Book? -- 1.5 What This Book Is Not About: Theory of Bayesian Statistics and Computation -- 1.6 Further Reading -- 1.7 Summary -- Chapter 2. Introduction to the Bayesian Analysis of a Statistical Model -- 2.1 Probability Theory and Statistics -- 2.2 Two Views of Statistics: Classical and Bayesian -- 2.3 The Importance of Modern Algorithms and Computers for Bayesian Statistics -- 2.4 Markov chain Monte Carlo (MCMC) and Gibbs Sampling -- 2.5 What Comes after MCMC? -- 2.6 Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model -- 2.7 Pointer to Special Topics in This Book -- 2.8 Summary -- Chapter 3. WinBUGS -- 3.1 What Is WinBUGS? -- 3.2 Running WinBUGS from R -- 3.3 WinBUGS Frees the Modeler in You -- 3.4 Some Technicalities and Conventions -- Chapter 4. A First Session in WinBUGS: The "Model of the Mean" -- 4.1 Introduction -- 4.2 Setting Up the Analysis -- 4.3 Starting the MCMC Blackbox -- 4.4 Summarizing the Results -- 4.5 Summary -- Chapter 5. Running WinBUGS from R via R2WinBUGS -- 5.1 Introduction -- 5.2 Data Generation -- 5.3 Analysis Using R -- 5.4 Analysis Using WinBUGS -- 5.5 Summary -- Chapter 6. Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor -- 6.1 Introduction -- 6.2 Stochastic Part of Linear Models: Statistical Distributions -- 6.3 Deterministic Part of Linear Models: Linear Predictor and Design Matrices -- 6.4 Summary -- Chapter 7. t-Test: Equal and Unequal Variances -- 7.1 t-Test with Equal Variances -- 7.2 t-Test with Unequal Variances.

7.3 Summary and a Comment on the Modeling of Variances -- Chapter 8. Normal Linear Regression -- 8.1 Introduction -- 8.2 Data Generation -- 8.3 Analysis Using R -- 8.4 Analysis Using WinBUGS -- 8.5 Summary -- Chapter 9. Normal One-Way ANOVA -- 9.1 Introduction: Fixed and Random Effects -- 9.2 Fixed-Effects ANOVA -- 9.3 Random-Effects ANOVA -- 9.4 Summary -- Chapter 10. Normal Two-Way ANOVA -- 10.1 Introduction: Main and Interaction Effects -- 10.2 Data Generation -- 10.3 Aside: Using Simulation to Assess Bias and Precision of an Estimator -- 10.4 Analysis Using R -- 10.5 Analysis Using WinBUGS -- 10.6 Summary -- Chapter 11. General Linear Model (ANCOVA) -- 11.1 Introduction -- 11.2 Data Generation -- 11.3 Analysis Using R -- 11.4 Analysis Using WinBUGS (And A Cautionary Tale About the Importance of Covariate Standardization) -- 11.5 Summary -- Chapter 12. Linear Mixed-Effects Model -- 12.1 Introduction -- 12.2 Data Generation -- 12.3 Analysis under a Random-Intercepts Model -- 12.4 Analysis under a Random-Coefficients Model without Correlation between Intercept and Slope -- 12.5 The Random-Coefficients Model with Correlation between Intercept and Slope -- 12.6 Summary -- Chapter 13. Introduction to the Generalized Linear Model: Poisson "t-test" -- 13.1 Introduction -- 13.2 An Important but Often Forgotten Issue with Count Data -- 13.3 Data Generation -- 13.4 Analysis Using R -- 13.5 Analysis Using WinBUGS -- 13.6 Summary -- Chapter 14. Overdispersion, Zero-Inflation, and Offsets in the GLM -- 14.1 Overdispersion -- 14.2 Zero-Inflation -- 14.3 Offsets -- 14.4 Summary -- Chapter 15. Poisson ANCOVA -- 15.1 Introduction -- 15.2 Data Generation -- 15.3 Analysis Using R -- 15.4 Analysis Using WinBUGS -- 15.5 Summary -- Chapter 16. Poisson Mixed-Effects Model (Poisson GLMM) -- 16.1 Introduction -- 16.2 Data Generation.

16.3 Analysis Under a Random-Coefficients Model -- 16.4 Summary -- Chapter 17. Binomial "t-Test" -- 17.1 Introduction -- 17.2 Data Generation -- 17.3 Analysis Using R -- 17.4 Analysis Using WinBUGS -- 17.5 Summary -- Chapter 18. Binomial Analysis of Covariance -- 18.1 Introduction -- 18.2 Data Generation -- 18.3 Analysis Using R -- 18.4 Analysis Using WinBUGS -- 18.5 Summary -- Chapter 19. Binomial Mixed-Effects Model (Binomial GLMM) -- 19.1 Introduction -- 19.2 Data Generation -- 19.3 Analysis Under a Random-Coefficients Model -- 19.4 Summary -- Chapter 20. Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model -- 20.1 Introduction -- 20.2 Data Generation -- 20.3 Analysis Using WinBUGS -- 20.4 Summary -- Chapter 21. Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance -- 21.1 Introduction -- 21.2 Data Generation -- 21.3 Analysis Using WinBUGS -- 21.4 Summary -- Chapter 22. Conclusions -- Appendix: A List of WinBUGS Tricks -- References -- Index.