Introduction to probability and statistics for ecosystem managers : simulation and resampling /

• Κύριος συγγραφέας: Haas, Timothy C.
• Άλλοι συγγραφείς: Lubar, Sheldon B.
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Chichester, West Sussex, United Kingdom : Wiley, 2013.
• Σειρά: Statistics in practice.
• Θέματα: Ecosystem management > Statistical methods.; BUSINESS & ECONOMICS > Development > Sustainable Development.; NATURE > Environmental Conservation & Protection.; Electronic books.

Πίνακας περιεχομένων:

• 1. Introduction
• 1.1. The textbook's purpose
• 1.1.1. The textbook's focus on ecosystem management
• 1.1.2. Reader level, prerequisites, and typical reader jobs
• 1.2. The textbook's 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 variable's 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.
• 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
• 8.2.2. Parameter estimation
• 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 influence 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 diagram's 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.