Environmental Statistics & Environmetrics
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Introduction to Probability and Statistics for Ecosystem Managers: Simulation and Resampling
Timothy C. Haas
(Author)
·
Wiley
· Hardcover
Introduction to Probability and Statistics for Ecosystem Managers: Simulation and Resampling - Haas, Timothy C.
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Synopsis "Introduction to Probability and Statistics for Ecosystem Managers: Simulation and Resampling"
Preface List Of Abbreviations List Of Tables List Of Figures 1 Introduction 1. 1 This Textbooks Purpose 1. 1. 1 The Textbooks Focus On Ecosystem Management 1. 1. 2 Reader Level, Prerequisites, And Typical Reader Jobs 1. 2 This Textbooks Pedagogical Approach 1. 2. 1 General Points 1. 2. 2 Use Of This Textbook For Self - Study 1. 2. 3 Learning Resources 1. 3 Chapter Summaries 1. 4 Installing And Running R Commander 1. 4. 1 Running R 1. 4. 2 Starting An R Commander Session 1. 4. 3 Terminating An R Commander Session 1. 5 Introductory R Commander Session 1. 6 Teaching Probability Through Simulation 1. 6. 1 The Frequentist Statistical Inference Paradigm 1. 7 Summary 2 Probability And Simulation 2. 1 Introduction 2. 2 Basic Probability 2. 2. 1 Definitions 2. 2. 2 Independence 2. 3 Random Variables 2. 3. 1 Definitions 2. 3. 2 Simulating Random Variables 2. 3. 3 A Random Variables Expected Value (Mean) And Variance 2. 3. 4 Details Of The Normal (Gaussian) Distribution 2. 3. 5 Distribution Approximations 2. 4 Joint Distributions 2. 4. 1 Definition 2. 4. 2 Mixed Variables 2. 4. 3 Marginal Distribution 2. 4. 4 Conditional Distributions 2. 4. 5 Independent Random Variables 2. 5 Influence Diagrams 2. 5. 1 Definitions 2. 5. 2 Example Of A Bayesian Network In Ecosystem Management 2. 5. 3 Modeling Causal Relationships With An Influence Diagram 2. 6 Advantages Of Influence Diagrams In Ecosystem Management 2. 7 Two Ecosystem Management Bayesian Networks 2. 7. 1 Waterbody Eutrophication 2. 7. 2 Wildlife Population Viability 2. 8 Influence Diagram Sensitivity Analysis 2. 9 Drawbacks To Influence Diagrams 3 Application Of Probability: Models Of Political Decision Making In Ecosystem Management 3. 1 Introduction 3. 2 Influence Diagram Models Of Decision Making 3. 2. 1 Ecosystem Status Perception Nodes 3. 2. 2 Image Nodes 3. 2. 3 Economic, Militaristic, And Institutional Goal Nodes 3. 2. 4 Audience Effect Nodes 3. 2. 5 Resource Nodes 3. 2. 6 Action And Target Nodes 3. 2. 7 Overall Goal Attainment Node 3. 2. 8 How A Group Influence Diagram Reaches A Decision 3. 2. 9 An Advantage Of This Decision Making Architecture 3. 2. 10 Evaluation Dimensions 3. 3 Rhino Poachers: A Simplified Model 3. 4 Policymakers: A Simplified Model 3. 5 Conclusions 4 Statistical Inference I: Basic Ideas And Parameter Estimation 4. 1 Definitions Of Some Fundamental Terms 4. 2 Estimating The Pdf And Cdf 4. 2. 1 Histograms 4. 2. 2 Ogive 4. 3 Measures Of Central Tendency And Dispersion 4. 4 Sample Quantiles 4. 4. 1 Sample Quartiles 4. 4. 2 Sample Deciles And Percentiles 4. 5 Distribution Of A Statistic 4. 5. 1 Basic Setup In Statistics 4. 5. 2 Sampling Distributions 4. 5. 3 Normal Quantile - Quantile Plot 4. 6 The Central Limit Theorem 4. 7 Parameter Estimation 4. 7. 1 Bias, Variance, And Efficiency 4. 8 Interval Estimates 4. 8. 1 A Confidence Interval For When 2 Is Known 4. 9 Basic Regression Analysis 4. 9. 1 Definitions And Fundamental Characteristics 4. 9. 2 The Regression Model 4. 9. 3 Correlation 4. 9. 4 Sampling Distributions 4. 9. 5 Prediction And Estimation 4. 9. 6 Misuse Of Regression Models 4. 10 General Methods Of Parameter Estimation 4. 10. 1 Maximum Likelihood 4. 10. 2 Minimum Hellinger Distance 4. 10. 3 Consistency Analysis 5 Statistical Inference Ii: Hypothesis Tests 5. 1 Introduction 5. 2 Hypothesis Tests: General Definitions And Properties 5. 2. 1 Definitions And Procedure 5. 2. 2 Confidence Intervals And Hypothesis Tests 5. 2. 3 Types Of Mistakes 5. 2. 4 One Way To Set The Tests Level 5. 2. 5 The Z - Test For Hypotheses About 5. 2. 6 P - Values 5. 3 Power 5. 3. 1 Power Curves 5. 4 T - Tests And A Test For Equal Variances 5. 4. 1 The T - Test 5. 4. 2 Two - Sample T - Tests 5. 4. 3 Tests For Paired Data 5. 4. 4 Testing For Equal Variances 5. 5 Hypothesis Tests On The Regression Model 5. 5. 1 Prediction And Estimation Confidence Intervals 5. 5. 2 Multiple Regression 5. 5. 3 Original Scale Prediction In Regression 5. 6 Brief Introduction To Vectors And Matrices 5. 6. 1 Basic Definitions 5. 6. 2 Inverse Of A Matrix 5. 6. 3 Random Vectors And Random Matrices 5. 7 Matrix Form Of Multiple Regression 5. 7. 1 Generalized Least Squares 5. 8 Hypothesis Testing With The Delete - D Jackknife 5. 8. 1 Background 5. 8. 2 A One - Sample Delete - D Jackknife Test 5. 8. 3 Testing Classifier Error Rates 5. 8. 4 Important Points About This Test 5. 8. 5 Parameter Confidence Intervals 6 Introduction To Spatial Statistics 6. 1 Overview 6. 1. 1 Types Of Spatial Processes 6. 2 Spatial Statistics And Gis 6. 2. 1 Types Of Spatial Data 6. 3 Qgis 6. 3. 1 Capabilities 6. 3. 2 Installing Qgis 6. 3. 3 Documentation And Tutorials 6. 3. 4 Installing Plugins 6. 3. 5 How To Convert A Text File To A Shapefile 6. 4 Continuous Spatial Processes 6. 4. 1 Definitions 6. 4. 2 Graphical Tools For Exploring Continuous Spatial Data 6. 4. 3 Third - And Fourth - Order Cumulant Minimization 6. 4. 4 Best Linear Unbiased Predictor 6. 4. 5 Kriging Variance 6. 4. 6 Model - Fitting Diagnostics 6. 4. 7 Kriging Within A Window 6. 5 Spatial Point Processes 6. 5. 1 Definitions 6. 5. 2 Marked Spatial Point Processes 6. 5. 3 Conclusions 6. 6 Continuously - Valued Multivariate Processes 6. 6. 1 Fitting Multivariate Covariance Functions 6. 6. 2 Cokriging: The Mwrck Procedure 7 Introduction To Spatio - Temporal Statistics 7. 1 Introduction 7. 2 Representing Time In A Gis 7. 2. 1 The Qgis Time Manager Plugin 7. 2. 2 A Clifford Algebra - Based Spatio - Temporal Data Structure 7. 2. 3 A Raster - And Event - Based Spatio - Temporal Data Model 7. 2. 4 Application Of Estdm To A Land Cover Study 7. 3 Spatio - Temporal Prediction: Mcstk 7. 3. 1 Algorithms 7. 3. 2 Covariogram Model And Its Estimator 7. 4 Multivariate Processes 7. 4. 1 Definitions 7. 4. 2 Transformations 7. 4. 3 Covariograms And Cross - Covariograms 7. 4. 4 Parameter Estimation 7. 4. 5 Prediction Algorithms 7. 4. 6 Cross - Validation 7. 4. 7 Summary 7. 5 Spatio - Temporal Point Processes 7. 6 Marked Spatio - Temporal Point Processes 7. 6. 1 A Mark Semivariogram Estimator 8 Application Of Statistical Inference: Estimating The Parameters Of An Individual - Based Model 8. 1 Overview. 8. 2 A Simple Ibm And Its Estimation 8. 2. 1 Simple Ibm 8. 3 Fitting Ibms With Mshd 8. 3. 1 Ergodicity 8. 3. 2 Observable Random Variables From Ibm Output 8. 4 Further Properties Of Parameter Estimators 8. 4. 1 Consistency 8. 4. 2 Robustness 8. 5 Parameter Confidence Intervals For A Nonergodic Model 8. 6 Rhino - Supporting Ecosystem Influence Diagram 8. 6. 1 Spatial Effects On Poaching 8. 6. 2 Ibm Variables 8. 6. 3 Initial Conditions And Hypothesis Values Of Parameters 8. 6. 4 Mapping Functions 8. 6. 5 Realism Of Ecosystem Influence Diagram Output 8. 7 Estimation Of Rhino Ibm Parameters 8. 7. 1 Parameter Confidence Intervals 9 Guiding An Inuence Diagram`S Learning 9. 1 Introduction 9. 2 Online Learning Of Bayesian Network Parameters 9. 2. 1 Basic Algorithm Using Simulation 9. 2. 2 Updating Influence Diagrams 9. 3 Learning An Influence Diagrams Structure 9. 3. 1 Minimum Description Length Score Function 9. 3. 2 Description Length Of An Edge 9. 3. 3 Random Generation Of Dags 9. 3. 4 Algorithm To Detect And Delete Cycles 9. 3. 5 Mutate Functions 9. 3. 6 Mdlep Algorithm 9. 3. 7 Using Mdlep To Learn Influence Diagram Structure 9. 4 Feedback - Based Learning For Group Decision Making Diagrams 9. 4. 1 Definitions And Algorithm 9. 5 Summary And Conclusions 10 Fitting And Testing A Political - Ecological Simulator 10. 1 Introduction 10. 1. 1 Background On Rhino Poaching 10. 1. 2 Scenarios Wherein Rhino Poaching Is Reduced 10. 2 Emt Simulator Construction 10. 2. 1 Modeled Groups 10. 2. 2 Rhino - Supporting Ecosystem Influence Diagram 10. 3 Consistency Analysis Estimates Of Simulator Parameters 10. 4 Mpemp Computation 10. 4. 1 Setup 10. 4. 2 Solution 10. 5 Conclusions Appendix A Simpson`S Rule In Two Dimensions References Index
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