An Introduction
Buch, Englisch, 152 Seiten, Lab Manual, Format (B × H): 216 mm x 280 mm, Gewicht: 402 g
ISBN: 978-0-691-12262-5
Verlag: Princeton University Press
The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data.Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Accompanying lab manual in R
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Biowissenschaften Terrestrische Ökologie
- Naturwissenschaften Biowissenschaften Biowissenschaften Meeres- und Süßwasserökologie
- Naturwissenschaften Biowissenschaften Biowissenschaften Ökologie
- Geowissenschaften Geologie Umweltgeologie, Geoökologie
- Geowissenschaften Geologie Paläoökologie
- Naturwissenschaften Biowissenschaften Biowissenschaften Naturschutzbiologie, Biodiversität
- Naturwissenschaften Physik Angewandte Physik Soziophysik, Wirtschaftsphysik
Weitere Infos & Material
Preface ix Part I. Introduction 1 Chapter 1: Models in Context 3
1.1 Complexity and Obscurity in Nature and in Models 3
1.2 Making the Connections: Data, Inference, and Decision 5
1.3 Two Elements of Models: Known and Unknown 13
1.4 Learning with Models: Hypotheses and Quantification 19
1.5 Estimation versus Forward Simulation 23
1.6 Statistical Pragmatism 24
Chapter 2: Model Elements: Application to Population Growth 27
2.1 A Model and Data Example 27
2.2 Model State and Time 30
2.3 Stochasticity for the Unknown 42
2.4 Additional Background on Process Models 44
Part II. Elements of Inference 45
Chapter 3: Point Estimation: Maximum Likelihood and the Method of Moments 3.1 Introduction 47
3.2 Likelihood 47
3.3 A Binomial Model 53
3.4 Combining the Binomial and Exponential 54
3.5 Maximum Likelihood Estimates for the Normal Distribution 56
3.6 Population Growth 57
3.7 Application: Fecundity 60
3.8 Survival Analysis Using Maximum Likelihood 62
3.9 Design Matrixes 68
3.10 Numerical Methods for MLE 71
3.11 Moment Matching 71
3.12 Common Sampling Distributions and Dispersion 74
3.13 Assumptions and Next Steps 76
Chapter 4: Elements of the Bayesian Approach 77
4.1 The Bayesian Approach 78
4.2 The Normal Distribution 84
4.3 Subjective Probability and the Role of the Prior 91
Chapter 5: Confidence Envelopes and Prediction Intervals 93
5.1 Classical Interval Estimation 95
5.2 Bayesian Credible Intervals 115
5.3 Likelihood Profile for Multiple Parameters 120
5.4 Confidence Intervals for Several Parameters: Linear Regression 122
5.5 Which Confidence Envelope to Use 130
5.6 Predictive Intervals 133
5.7 Uncertainty and Variability 141
5.8 When Is It Bayesian? 142
Chapter 6: Model Assessment and Selection 143
6.1 Using Statistics to Evaluate Models 143
6.2 The Role of Hypothesis Tests 144
6.3 Nested Models 144
6.4 Additional Considerations for Classical Model Selection 151
6.5 Bayesian Model Assessment 154
6.6 Additional Thoughts on Bayesian Model Assessment 159
Part III. Larger Models 161
Chapter 7: Computational Bayes: Introduction to Tools Simulation 163
7.1 Simulation to Obtain the Posterior 163
7.2 Some Basic Simulation Techniques 164
7.3 Markov Chain Monte Carlo Simulation 173
7.4 Application: Bayesian Analysis for Regression 189
7.5 Using MCMC 202
7.6 Computation for Bayesian Model Selection 205
7.7 Priors on the Response 209
7.8 The Basics Are Now Behind Us 212
Chapter 8: A Closer Look at Hierarchical Structures 213
8.1 Hierarchical Models for Context 213
8.2 Mixed and Generalized Linear Models 216
8.3 Application: Growth Responses to CO2 230
8.4 Thinking Conditionally 235
8.5 Two Applications to Trees 241
8.6 Noninformative Priors in Hierarchical Settings 249
8.7 From Simple Models to Graphs 249
Part IV. More Advance Methods 251
Chapter 9: Time 9.1 Why Is Time Important? 253
9.2 Time Series Terminology 254
9.3 Descriptive Elements of Time Series Models 255
9.4 The Frequency Domain 264
9.5 Application: Detecting Density Dependence in Population Time Series 264
9.6 Bayesian State Space Models 272
9.7 Application: Black Noddy on Heron Island 282
9.8 Nonlinear State Space Models 289
9.9 Lags 297
9.10 Regime Change 298
9.11 Constraints on Time Series Data 300
9.12 Additional Sources of Variablity 301
9.13 Alternatives to the Gibbs Sampler 302
9.14 More on Longitudinal Data Structures 302
9.15 Intervention and Treatment Effects 309
9.16 Capture-Recapture Studies 318
9.17 Structured Models as Matrixes 329
9.18 Structure as Systems of Difference Equations 336
9.19 Time Series, Population Regulation, and Stochasticity 347
Chapter 10: Space-Time 353
10.1 A Deterministic Model for a Stochastic Spatial Process 354
10.2 Classical Inference on Population Movement 359
10.3 Island Biogeography and Metapopulations 378
10.4 Estimation of Passive Disp
