Note: Cover Page -- Title page -- Half-title page -- Copyright page -- Contents -- Preface -- Part I: Introduction -- 1. Models in Context -- 1.1 Complexity and Obscurity in Nature and in Models -- 1.2 Making the Connections: Data, Inference, and Decision -- 1.3 Two Elements of Models: Known and Unknown -- 1.4 Learning with Models: Hypotheses and Quantification -- 1.5 Estimation versus Forward Simulation -- 1.6 Statistical Pragmatism -- 2. Model Elements: Application to Population Growth -- 2.1 A Model and Data Example -- 2.2 Model State and Time -- 2.3 Stochasticity for the Unknown -- 2.4 Additional Background on Process Models -- Part II: Elements of Inference -- 3. Point Estimation: Maximum Likelihood and the Method of Moments -- 3.1 Introduction -- 3.2 Likelihood -- 3.3 A Binomial Model -- 3.4 Combining the Binomial and Exponential -- 3.5 Maximum Likelihood Estimates for the Normal Distribution -- 3.6 Population Growth -- 3.7 Application: Fecundity -- 3.8 Survival Analysis Using Maximum Likelihood -- 3.9 Design Matrixes -- 3.10 Numerical Methods for MLE -- 3.11 Moment Matching -- 3.12 Common Sampling Distributions and Dispersion -- 3.13 Assumptions and Next Steps -- 4. Elements of the Bayesian Approach -- 4.1 The Bayesian Approach -- 4.2 The Normal Distribution -- 4.3 Subjective Probability and the Role of the Prior -- 5. Confidence Envelopes and Prediction Intervals -- 5.1 Classical Interval Estimation -- 5.2 Bayesian Credible Intervals -- 5.3 Likelihood Profile for Multiple Parameters -- 5.4 Confidence Intervals for Several Parameters: Linear Regression -- 5.5 Which Confidence Envelope to Use -- 5.6 Predictive Intervals -- 5.7 Uncertainty and Variability -- 5.8 When Is It Bayesian? -- 6. Model Assessment and Selection -- 6.1 Using Statistics to Evaluate Models -- 6.2 The Role of Hypothesis Tests -- 6.3 Nested Models. , 6.4 Additional Considerations for Classical Model Selection -- 6.5 Bayesian Model Assessment -- 6.6 Additional Thoughts on Bayesian Model Assessment -- Part III: Larger Models -- 7. Computational Bayes: Introduction to Tools Simulation -- 7.1 Simulation to Obtain the Posterior -- 7.2 Some Basic Simulation Techniques -- 7.3 Markov Chain Monte Carlo Simulation -- 7.4 Application: Bayesian Analysis for Regression -- 7.5 Using MCMC -- 7.6 Computation for Bayesian Model Selection -- 7.7 Priors on the Response -- 7.8 The Basics Are Now Behind Us -- 8. A Closer Look at Hierarchical Structures -- 8.1 Hierarchical Models for Context -- 8.2 Mixed and Generalized Linear Models -- 8.3 Application: Growth Responses to CO2 -- 8.4 Thinking Conditionally -- 8.5 Two Applications to Trees -- 8.6 Noninformative Priors in Hierarchical Settings -- 8.7 From Simple Models to Graphs -- Part IV: More Advance Methods -- 9. Time -- 9.1 Why Is Time Important? -- 9.2 Time Series Terminology -- 9.3 Descriptive Elements of Time Series Models -- 9.4 The Frequency Domain -- 9.5 Application: Detecting Density Dependence in Population Time Series -- 9.6 Bayesian State Space Models -- 9.7 Application: Black Noddy on Heron Island -- 9.8 Nonlinear State Space Models -- 9.9 Lags -- 9.10 Regime Change -- 9.11 Constraints on Time Series Data -- 9.12 Additional Sources of Variablity -- 9.13 Alternatives to the Gibbs Sampler -- 9.14 More on Longitudinal Data Structures -- 9.15 Intervention and Treatment Effects -- 9.16 Capture-Recapture Studies -- 9.17 Structured Models as Matrixes -- 9.18 Structure as Systems of Difference Equations -- 9.19 Time Series, Population Regulation, and Stochasticity -- 10. Space-Time -- 10.1 A Deterministic Model for a Stochastic Spatial Process -- 10.2 Classical Inference on Population Movement -- 10.3 Island Biogeography and Metapopulations. , 10.4 Estimation of Passive Dispersal -- 10.5 A Bayesian Framework -- 10.6 Models for Explicit Space -- 10.7 Point-Referenced Data -- 10.8 Block-Referenced Data and Misalignment -- 10.9 Hierarchical Treatment of Space -- 10.10 Application: A Spatio-Temporal Model of Population Spread -- 10.11 How to Handle Space -- 11. Some Concluding Perspectives -- 11.1 Models, Data, and Decision -- 11.2 The Promise of Graphical Models, Improved Algorithms, and Faster Computers -- 11.3 Predictions and What to Do with Them -- 11.4 Some Remarks on Software -- Appendix A. Taylor Series -- Appendix B. Some Notes on Differential and Difference Equations -- Appendix C. Basic Matrix Algebra -- Appendix D. Probability Models -- Appendix E. Basic Life History Calculations -- Appendix F. Common Distributions -- Appendix G. Common Conjugate Likelihood-Prior Pairs -- References -- Index.

Note: Intro -- Contents -- Preface -- Contributors -- Part I: Introduction to hierarchical modeling -- 1 Elements of hierarchical Bayesian inference -- 2 Bayesian hierarchical models in geographical genetics -- Part II: Hierarchical models in experimental settings -- 3 Synthesizing ecological experiments and observational data with hierarchical Bayes -- 4 Effects of global change on inflorescence production: a Bayesian hierarchical analysis -- Part III: Spatial modeling -- 5 Building statistical models to analyze species distributions -- 6 Implications of vulnerability to hurricane damage for long-term survival of tropical tree species: a Bayesian hierarchical analysis -- Part IV: Spatio-temporal modeling -- 7 Spatial-temporal statistical modeling and prediction of environmental processes -- 8 Hierarchical Bayesian spatio-temporal models for population spread -- 9 Spatial models for the distribution of extremes -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.