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Ecological forecasting / Michael C. Dietze.

By: Dietze, Michael Christopher, 1976- [author.].
Material type: materialTypeLabelBookPublisher: Princeton : Princeton University Press, 2017Description: x, 270 pages : illustrations ; 27 cm.ISBN: 9780691160573; 0691160570.Subject(s): ECOSYSTEM MANAGEMENT | ECOSYSTEM HEALTH | PREDICTION | ECOLOGY | BAYESIAN STATISTICS | KALMAN FILTERSHoldings: GRETA POINT: 574.087.1 DIE
Contents:
1. Introduction; 1.1 Why Forecast?; 1.2 The Informatics Challenge in Forecasting; 1.3 The Model-Data Loop; 1.4 Why Bayes?; 1.5 Models as Scaffolds; 1.6 Case Studies and Decision Support; 1.7 Key Concepts; 1.8 Hands-on Activities; 2. From Models to Forecasts; 2.1 The Traditional Modeler's Toolbox; 2.2 Example: The Logistic Growth Model; 2.3 Adding Sources of Uncertainty; 2.4 Thinking Probabilistically; 2.5 Predictability; 2.6 Key Concepts; 2.7 Hands-on Activities; 3. Data, Large and Small; 3.1 The Data Cycle and Best Practices
3.2 Data Standards and Metadata3.3 Handling Big Data; 3.4 Key Concepts; 3.5 Hands-on Activities; 4. Scientific Workflows and the Informatics of Model-Data Fusion; 4.1 Transparency, Accountability, and Repeatability; 4.2 Workflows and Automation; 4.3 Best Practices for Scientific Computing; 4.4 Key Concepts; 4.5 Hands-on Activities; 5. Introduction to Bayes; 5.1 Confronting Models with Data; 5.2 Probability 101; 5.3 The Likelihood; 5.4 Bayes' Theorem; 5.5 Prior Information; 5.6 Numerical Methods for Bayes; 5.7 Evaluating MCMC Output; 5.8 Key Concepts; 5.9 Hands-on Activities
6. Characterizing Uncertainty6.1 Non-Gaussian Error; 6.2 Heteroskedasticity; 6.3 Observation Error; 6.4 Missing Data and Inverse Modeling; 6.5 Hierarchical Models and Process Error; 6.6 Autocorrelation; 6.7 Key Concepts; 6.8 Hands-on Activities; 7. Case Study: Biodiversity, Populations, and Endangered Species; 7.1 Endangered Species; 7.2 Biodiversity; 7.3 Key Concepts; 7.4 Hands-on Activities; 8. Latent Variables and State-Space Models; 8.1 Latent Variables; 8.2 State Space; 8.3 Hidden Markov Time-Series Model; 8.4 Beyond Time; 8.5 Key Concepts; 8.6 Hands-on Activities; 9. Fusing Data Sources
9.1 Meta-analysis9.2 Combining Data: Practice, Pitfalls, and Opportunities; 9.3 Combining Data and Models across Space and Time; 9.4 Key Concepts; 9.5 Hands-on Activities; 10. Case Study: Natural Resources; 10.1 Fisheries; 10.2 Case Study: Baltic Salmon; 10.3 Key Concepts; 11. Propagating, Analyzing, and Reducing Uncertainty; 11.1 Sensitivity Analysis; 11.2 Uncertainty Propagation; 11.3 Uncertainty Analysis; 11.4 Tools for Model-Data Feedbacks; 11.5 Key Concepts; 11.6 Hands-on Activities; Appendix A Properties of Means and Variances; Appendix B Common Variance Approximations
12. Case Study: Carbon Cycle12.1 Carbon Cycle Uncertainties; 12.2 State of the Science; 12.3 Case Study: Model-Data Feedbacks; 12.4 Key Concepts; 12.5 Hands-on Activities; 13. Data Assimilation 1: Analytical Methods; 13.1 The Forecast Cycle; 13.2 Kalman Filter; 13.3 Extended Kalman Filter; 13.4 Key Concepts; 13.5 Hands-on Activities; 14. Data Assimilation 2: Monte Carlo Methods; 14.1 Ensemble Filters; 14.2 Particle Filter; 14.3 Model Averaging and Reversible Jump MCMC; 14.4 Generalizing the Forecast Cycle; 14.5 Key Concepts; 14.6 Hands-on Activities; 15. Epidemiology; 15.1 Theory
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BOOK BOOK WELLINGTON
BOOKS
574.087.1 DIE 1 Issued 24/01/2019 B018630

Includes bibliographical references and index.

1. Introduction; 1.1 Why Forecast?; 1.2 The Informatics Challenge in Forecasting; 1.3 The Model-Data Loop; 1.4 Why Bayes?; 1.5 Models as Scaffolds; 1.6 Case Studies and Decision Support; 1.7 Key Concepts; 1.8 Hands-on Activities; 2. From Models to Forecasts; 2.1 The Traditional Modeler's Toolbox; 2.2 Example: The Logistic Growth Model; 2.3 Adding Sources of Uncertainty; 2.4 Thinking Probabilistically; 2.5 Predictability; 2.6 Key Concepts; 2.7 Hands-on Activities; 3. Data, Large and Small; 3.1 The Data Cycle and Best Practices

3.2 Data Standards and Metadata3.3 Handling Big Data; 3.4 Key Concepts; 3.5 Hands-on Activities; 4. Scientific Workflows and the Informatics of Model-Data Fusion; 4.1 Transparency, Accountability, and Repeatability; 4.2 Workflows and Automation; 4.3 Best Practices for Scientific Computing; 4.4 Key Concepts; 4.5 Hands-on Activities; 5. Introduction to Bayes; 5.1 Confronting Models with Data; 5.2 Probability 101; 5.3 The Likelihood; 5.4 Bayes' Theorem; 5.5 Prior Information; 5.6 Numerical Methods for Bayes; 5.7 Evaluating MCMC Output; 5.8 Key Concepts; 5.9 Hands-on Activities

6. Characterizing Uncertainty6.1 Non-Gaussian Error; 6.2 Heteroskedasticity; 6.3 Observation Error; 6.4 Missing Data and Inverse Modeling; 6.5 Hierarchical Models and Process Error; 6.6 Autocorrelation; 6.7 Key Concepts; 6.8 Hands-on Activities; 7. Case Study: Biodiversity, Populations, and Endangered Species; 7.1 Endangered Species; 7.2 Biodiversity; 7.3 Key Concepts; 7.4 Hands-on Activities; 8. Latent Variables and State-Space Models; 8.1 Latent Variables; 8.2 State Space; 8.3 Hidden Markov Time-Series Model; 8.4 Beyond Time; 8.5 Key Concepts; 8.6 Hands-on Activities; 9. Fusing Data Sources

9.1 Meta-analysis9.2 Combining Data: Practice, Pitfalls, and Opportunities; 9.3 Combining Data and Models across Space and Time; 9.4 Key Concepts; 9.5 Hands-on Activities; 10. Case Study: Natural Resources; 10.1 Fisheries; 10.2 Case Study: Baltic Salmon; 10.3 Key Concepts; 11. Propagating, Analyzing, and Reducing Uncertainty; 11.1 Sensitivity Analysis; 11.2 Uncertainty Propagation; 11.3 Uncertainty Analysis; 11.4 Tools for Model-Data Feedbacks; 11.5 Key Concepts; 11.6 Hands-on Activities; Appendix A Properties of Means and Variances; Appendix B Common Variance Approximations

12. Case Study: Carbon Cycle12.1 Carbon Cycle Uncertainties; 12.2 State of the Science; 12.3 Case Study: Model-Data Feedbacks; 12.4 Key Concepts; 12.5 Hands-on Activities; 13. Data Assimilation 1: Analytical Methods; 13.1 The Forecast Cycle; 13.2 Kalman Filter; 13.3 Extended Kalman Filter; 13.4 Key Concepts; 13.5 Hands-on Activities; 14. Data Assimilation 2: Monte Carlo Methods; 14.1 Ensemble Filters; 14.2 Particle Filter; 14.3 Model Averaging and Reversible Jump MCMC; 14.4 Generalizing the Forecast Cycle; 14.5 Key Concepts; 14.6 Hands-on Activities; 15. Epidemiology; 15.1 Theory

GRETA POINT: 574.087.1 DIE

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