{"product_id":"statistical-computation-for-environmental-scienc-lab-manual-for-models-for-ecological-data-9780691122625","title":"Statistical Computation for Environmental Scienc","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntroduces ecologists to modern methods in modeling and computation. This book covers both classical statistical approaches and computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. It introduces readers to the practical work of data modeling and computation in the language R.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"In summary, Models for Ecological Data is an important text for those interested in ecological problems, which require computationally intensive methods. The level of the text is such that the reader should have a strong quantitative background (masters degree or higher in a quantitative discipline). The accompanying lab manual is a must for those who have this text and want to put the material to practice. The text and accompanying lab manual would serve as a good textbook for a graduate course in quantitative ecology provided that the students have the necessary mathematical background.\"--Timothy J. Robinson, Journal of the American Statistical Association\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface 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 Dispersal 388  10.5 A Bayesian Framework 397  10.6 Models for Explicit Space 401  10.7 Point-Referenced Data 403  10.8 Block-Referenced Data and Misalignment 412  10.9 Hierarchical Treatment of Space 415  10.10 Application: A Spatio-Temporal Model of Population Spread 424  10.11 How to Handle Space 432      Chapter 11: Some Concluding Perspectives 435  11.1 Models, Data, and Decision 435  11.2 The Promise of Graphical Models, Improved Algorithms, and Faster Computers 437  11.3 Predictions and What to Do with Them 444  11.4 Some Remarks on Software 456      Appendix A Taylor Series 457  Appendix B Some Notes on Differential and Difference Equations 464  Appendix C Basic Matrix Algebra 486  Appendix D Probability Models 502  Appendix E Basic Life History Calculations 541  Appendix F Common Distributions 573  Appendix G Common Conjugate Likelihood-Prior Pairs 583      References 585  Index 615","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":51359106171223,"sku":"9780691122625","price":28.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691122625.jpg?v=1754123611","url":"https:\/\/bookcurl.com\/products\/statistical-computation-for-environmental-scienc-lab-manual-for-models-for-ecological-data-9780691122625","provider":"Book Curl","version":"1.0","type":"link"}