Introduction to probability and statistics for ecosystem managers : simulation and resampling / Timothy C. Haas, Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, USA.

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...

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Bibliographic Details
Online Access:	Full Text (via Wiley)
Main Author:	Haas, Timothy C.
Other Authors:	Lubar, Sheldon B.
Format:	eBook
Language:	English
Published:	Chichester, West Sussex, United Kingdom : Wiley, 2013.
Series:	Statistics in practice.
Subjects:	Ecosystem management > Statistical methods.

Table of Contents:

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.
Machine generated contents note: 1. Introduction
1.1. textbook's purpose
1.1.1. textbook's focus on ecosystem management
1.1.2. Reader level, prerequisites, and typical reader jobs
1.2. 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. 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. 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. 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. central limit theorem
4.7. Parameter estimation
4.7.1. Bias, variance, and efficiency
4.8. Interval estimates
4.8.1. confidence interval for μ when σ2 is known
4.9. Basic regression analysis
4.9.1. Definitions and fundamental characteristics
4.9.2. 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 test's level
5.2.5. z-test for hypotheses about μ
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. 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. 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. QGIS Time Manager plugin
7.2.2. Clifford algebra-based spatio-temporal data structure
7.2.3. 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. mark semivariogram estimator
8. Application of statistical inference: Estimating the parameters of an individual-based model
8.1. Overview
8.2. 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.

Introduction to probability and statistics for ecosystem managers : simulation and resampling / Timothy C. Haas, Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, USA.

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