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Bayesian analysis for population ecology / Ruth King [and others].

By: Material type: TextTextSeries: Interdisciplinary statisticsPublisher: Boca Raton : Chapman & Hall/CRC, [2010]Copyright date: ©2010Description: xiii, 442 pages : illustrations ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 1439811873
  • 9781439811870
Subject(s): DDC classification:
  • 577.8801519542 22
LOC classification:
  • QH352 .B38 2010
Contents:
Part I. Introduction to Statistical Analysis of Ecological Data -- 1. Introduction -- 1.1. Population Ecology -- 1.2. Conservation and Management -- 1.3. Data and Models -- 1.4. Bayesian and Classical Statistical Inference -- 1.5. Senescence -- 1.6. Summary -- 1.7. Further Reading -- 1.8. Exercises -- 2. Data, Models and Likelihoods -- 2.1. Introduction -- 2.2. Population Data -- 2.3. Modelling Survival -- 2.4. Multi-Site, Multi-State and Movement Data -- 2.5. Covariates and Large Data Sets -- 2.6. Combining Information -- 2.7. Modelling Productivity -- 2.8. Parameter Redundancy -- 2.9. Summary -- 2.10. Further Reading -- 2.11. Exercises -- 3. Classical Inference Based on Likelihood -- 3.1. Introduction -- 3.2. Simple Likelihoods -- 3.3. Model Selection -- 3.4. Maximising Log-Likelihoods -- 3.5. Confidence Regions -- 3.6. Computer Packages -- 3.7. Summary -- 3.8. Further Reading -- 3.9. Exercises -- Part II. Bayesian Techniques and Tools -- 4. Bayesian Inference -- 4.1. Introduction -- 4.2. Prior Selection and Elicitation -- 4.3. Prior Sensitivity Analyses -- 4.4. Summarising Posterior Distributions -- 4.5. Directed Acyclic Graphs -- 4.6. Summary -- 4.7. Further Reading -- 4.8. Exercises -- 5. Markov Chain Monte Carlo -- 5.1. Monte Carlo Integration -- 5.2. Markov Chains -- 5.3. Markov Chain Monte Carlo -- 5.4. Implementing MCMC -- 5.5. Summary -- 5.6. Further Reading -- 5.7. Exercises -- 6. Model Discrimination -- 6.1. Introduction -- 6.2. Bayesian Model Discrimination -- 6.3. Estimating Posterior Model Probabilities -- 6.4. Prior Sensitivity -- 6.5. Model Averaging -- 6.6. MarginalPosterior Distributions -- 6.7. Assessing Temporal /Age Dependence -- 6.8. Improving and Checking Performance -- 6.9. Additional Computational Techniques -- 6.10. Summary -- 6.11. Further Reading -- 6.12. Exercises -- 7. MCMC and RJMCMC Computer Programs -- 7.1. R Code (MCMC) for Dipper Data -- 7.2. WinBUGS Code (MCMC) for Dipper Data -- 7.3. MCMC within the Computer Package MARK -- 7.4. R code (RJMCMC) for Model Uncertainty -- 7.5. WinBUGS Code (RJMCMC) for Model Uncertainty -- 7.6. Summary -- 7.7. Further Reading -- 7.8. Exercises -- Part III. Ecological Applications -- 8. Covariates, Missing Values and Random Effects -- 8.1. Introduction -- 8.2. Covariates -- 8.3. Missing Values -- 8.4. Assessing Covariate Dependence -- 8.5. Random Effects -- 8.6. Prediction -- 8.7. Splines -- 8.8. Summary -- 8.9. Further Reading -- 9. Multi-State Models -- 9.1. Introduction -- 9.2. Missing Covariate /Auxiliary Variable Approach -- 9.3. Model Discrimination and Averaging -- 9.4. Summary -- 9.5. Further Reading -- 10. State-Space Modelling -- 10.1. Introduction -- 10.2. Leslie Matrix-Based Models -- 10.3. Non-Leslie-Based Models -- 10.4. Capture-Recapture Data -- 10.5. Summary -- 10.6. Further Reading -- 11. Closed Populations -- 11.1. Introduction -- 11.2. Models and Notation -- 11.3. Model Fitting -- 11.4. Model Discrimination and Averaging -- 11.5. Line Transects -- 11.6. Summary -- 11.7. Further Reading -- Appendix A. Common Distributions -- Appendix A.1. Discrete Distributions -- Appendix A.2. Continuous Distributions -- Appendix B. Programming in R -- Appendix B.1. Getting Started in R -- Appendix B.2. Useful R Commands -- Appendix B.3. Writing (RJ)MCMC Functions -- Appendix B.4. R Code for Model C /C -- Appendix B.5. R Code for White Stork Covariate Analysis -- Appendix B.6. Summary -- Appendix C. Programming in WinBUGS -- Appendix C.1. WinBUGS -- Appendix C.2. Calling WinBUGS from R -- Appendix C.3. Summary.
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Item type Current library Call number Copy number Status Date due Barcode
Book City Campus City Campus Main Collection 577.8801519542 BAY (Browse shelf(Opens below)) 1 Available A454345B

Includes bibliographical references and index.

Part I. Introduction to Statistical Analysis of Ecological Data -- 1. Introduction -- 1.1. Population Ecology -- 1.2. Conservation and Management -- 1.3. Data and Models -- 1.4. Bayesian and Classical Statistical Inference -- 1.5. Senescence -- 1.6. Summary -- 1.7. Further Reading -- 1.8. Exercises -- 2. Data, Models and Likelihoods -- 2.1. Introduction -- 2.2. Population Data -- 2.3. Modelling Survival -- 2.4. Multi-Site, Multi-State and Movement Data -- 2.5. Covariates and Large Data Sets -- 2.6. Combining Information -- 2.7. Modelling Productivity -- 2.8. Parameter Redundancy -- 2.9. Summary -- 2.10. Further Reading -- 2.11. Exercises -- 3. Classical Inference Based on Likelihood -- 3.1. Introduction -- 3.2. Simple Likelihoods -- 3.3. Model Selection -- 3.4. Maximising Log-Likelihoods -- 3.5. Confidence Regions -- 3.6. Computer Packages -- 3.7. Summary -- 3.8. Further Reading -- 3.9. Exercises -- Part II. Bayesian Techniques and Tools -- 4. Bayesian Inference -- 4.1. Introduction -- 4.2. Prior Selection and Elicitation -- 4.3. Prior Sensitivity Analyses -- 4.4. Summarising Posterior Distributions -- 4.5. Directed Acyclic Graphs -- 4.6. Summary -- 4.7. Further Reading -- 4.8. Exercises -- 5. Markov Chain Monte Carlo -- 5.1. Monte Carlo Integration -- 5.2. Markov Chains -- 5.3. Markov Chain Monte Carlo -- 5.4. Implementing MCMC -- 5.5. Summary -- 5.6. Further Reading -- 5.7. Exercises -- 6. Model Discrimination -- 6.1. Introduction -- 6.2. Bayesian Model Discrimination -- 6.3. Estimating Posterior Model Probabilities -- 6.4. Prior Sensitivity -- 6.5. Model Averaging -- 6.6. MarginalPosterior Distributions -- 6.7. Assessing Temporal /Age Dependence -- 6.8. Improving and Checking Performance -- 6.9. Additional Computational Techniques -- 6.10. Summary -- 6.11. Further Reading -- 6.12. Exercises -- 7. MCMC and RJMCMC Computer Programs -- 7.1. R Code (MCMC) for Dipper Data -- 7.2. WinBUGS Code (MCMC) for Dipper Data -- 7.3. MCMC within the Computer Package MARK -- 7.4. R code (RJMCMC) for Model Uncertainty -- 7.5. WinBUGS Code (RJMCMC) for Model Uncertainty -- 7.6. Summary -- 7.7. Further Reading -- 7.8. Exercises -- Part III. Ecological Applications -- 8. Covariates, Missing Values and Random Effects -- 8.1. Introduction -- 8.2. Covariates -- 8.3. Missing Values -- 8.4. Assessing Covariate Dependence -- 8.5. Random Effects -- 8.6. Prediction -- 8.7. Splines -- 8.8. Summary -- 8.9. Further Reading -- 9. Multi-State Models -- 9.1. Introduction -- 9.2. Missing Covariate /Auxiliary Variable Approach -- 9.3. Model Discrimination and Averaging -- 9.4. Summary -- 9.5. Further Reading -- 10. State-Space Modelling -- 10.1. Introduction -- 10.2. Leslie Matrix-Based Models -- 10.3. Non-Leslie-Based Models -- 10.4. Capture-Recapture Data -- 10.5. Summary -- 10.6. Further Reading -- 11. Closed Populations -- 11.1. Introduction -- 11.2. Models and Notation -- 11.3. Model Fitting -- 11.4. Model Discrimination and Averaging -- 11.5. Line Transects -- 11.6. Summary -- 11.7. Further Reading -- Appendix A. Common Distributions -- Appendix A.1. Discrete Distributions -- Appendix A.2. Continuous Distributions -- Appendix B. Programming in R -- Appendix B.1. Getting Started in R -- Appendix B.2. Useful R Commands -- Appendix B.3. Writing (RJ)MCMC Functions -- Appendix B.4. R Code for Model C /C -- Appendix B.5. R Code for White Stork Covariate Analysis -- Appendix B.6. Summary -- Appendix C. Programming in WinBUGS -- Appendix C.1. WinBUGS -- Appendix C.2. Calling WinBUGS from R -- Appendix C.3. Summary.

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