The Resource 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

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

Label
Introduction to probability and statistics for ecosystem managers : simulation and resampling
Title
Introduction to probability and statistics for ecosystem managers
Title remainder
simulation and resampling
Statement of responsibility
Timothy C. Haas, Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, USA
Creator
Contributor
Subject
Language
eng
Member of
Cataloging source
DLC
Dewey number
333.72
Index
index present
LC call number
QH77.3.S73
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
Statistics in practice
Label
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
Publication
Bibliography note
Includes bibliographical references and index
http://library.link/vocab/branchCode
  • net
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
  • 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
  • 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
Control code
ocn841051232
Extent
1 online resource
Form of item
online
Isbn
9781118636206
Lccn
2013015799
Media category
computer
Media MARC source
rdamedia
Media type code
c
http://library.link/vocab/recordID
.b28791198
Specific material designation
remote
System control number
  • (OCoLC)841051232
  • pebcs1118636201

Library Locations

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