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Introduction to Probability and Statistics for Ecosystem Managers : Simulation and Resampling.
Explores computer-intensive probability and statistics for ecosystem management decision making Simulation is an accessible way to explain probability and stochastic model behavior to beginners. This book introduces probability and statistics to future and practicing ecosystem managers by providing...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2013.
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| Colección: | Statistics in practice.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Statistics in Practice; Title Page; Copyright; Dedication; List of figures; List of tables; Preface; Acknowledgments; List of abbreviations; Chapter 1: Introduction; 1.1 The textbook's purpose; 1.2 The textbook's pedagogical approach; 1.3 Chapter summaries; 1.4 Installing and running R Commander; 1.5 Introductory R Commander session; 1.6 Teaching probability through simulation; 1.7 Summary; Chapter 2: Probability and simulation; 2.1 Introduction; 2.2 Basic probability; 2.3 Random variables; 2.4 Joint distributions; 2.5 Influence diagrams.
- 2.6 Advantages of influence diagrams in ecosystem management2.7 Two ecosystem management Bayesian networks; 2.8 Influence diagram sensitivity analysis; 2.9 Drawbacks to influence diagrams; Chapter 3: Application of probability: Models of political decision making in ecosystem management; 3.1 Introduction; 3.2 Influence diagram models of decision making; 3.3 Rhino poachers: A simplified model; 3.4 Policymakers: A simplified model; 3.5 Conclusions; Chapter 4: Statistical inference I: Basic ideas and parameter estimation; 4.1 Definitions of some fundamental terms; 4.2 Estimating the PDF and CDF.
- 4.3 Measures of central tendency and dispersion4.4 Sample quantiles; 4.5 Distribution of a statistic; 4.6 The central limit theorem; 4.7 Parameter estimation; 4.8 Interval estimates; 4.9 Basic regression analysis; 4.10 General methods of parameter estimation; Chapter 5: Statistical inference II: Hypothesis tests; 5.1 Introduction; 5.2 Hypothesis tests: General definitions and properties; 5.3 Power; 5.4 t-Tests and a test for equal variances; 5.5 Hypothesis tests on the regression model; 5.6 Brief introduction to vectors and matrices; 5.7 Matrix form of multiple regression.
- 5.8 Hypothesis testing with the delete-d jackknifeChapter 6: Introduction to spatial statistics; 6.1 Overview; 6.2 Spatial statistics and GIS; 6.3 QGIS; 6.4 Continuous spatial processes; 6.5 Spatial point processes; 6.6 Continuously valued multivariate processes; Chapter 7: Introduction to spatio-temporal statistics; 7.1 Introduction; 7.2 Representing time in a GIS; 7.3 Spatio-temporal prediction: MCSTK; 7.4 Multivariate processes; 7.5 Spatio-temporal point processes; 7.6 Marked spatio-temporal point processes.
- Chapter 8: Application of statistical inference: Estimating the parameters of an individual-based model8.1 Overview; 8.2 A simple IBM and its estimation; 8.3 Fitting IBMs with MSHD; 8.4 Further properties of parameter estimators; 8.5 Parameter confidence intervals for a nonergodic model; 8.6 Rhino-supporting ecosystem influence diagram; 8.7 Estimation of rhino IBM parameters; Chapter 9: Guiding an influence diagram's learning; 9.1 Introduction; 9.2 Online learning of Bayesian network parameters; 9.3 Learning an influence diagram's structure.