Call number:
PIK N 531-17-91098
Type of Medium:
Monograph available for loan
Pages:
x. 270 Seiten
,
Diagramme
ISBN:
9781400885459
,
9780691160573 (print)
Language:
English
Note:
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 Metadata -- 3.3 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 Uncertainty -- 6.1 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-analysis -- 9.2 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 Cycle -- 12.1 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 -- 15.2 Ecological Forecasting -- 15.3 Examples of Epidemiological Forecasting -- 15.4 Case Study: Influenza -- 15.5 Key Concepts -- 16. Assessing Model Performance -- 16.1 Visualization -- 16.2 Basic Model Diagnostics -- 16.3 Model Benchmarks -- 16.4 Data Mining the Residuals -- 16.5 Comparing Model Performance to Simple Statistics -- 16.6 Key Concepts -- 16.7 Hands-on Activities -- 17. Projection and Decision Support -- 17.1 Projections, Predictions, and Forecasting -- 17.2 Decision Support -- 17.3 Key Concepts -- 17.4 Hands-on Activities -- 18. Final Thoughts -- 18.1 Lessons Learned -- 18.2 Future Directions -- References -- Index
Location:
A 18 - must be ordered
Branch Library:
PIK Library
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