Statistical and mathematical models are defined by parameters that describe different characteristics of those models. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.
Statistical and mathematical models are defined by parameters that describe different characteristics of those models. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.
Diana Cole is a Senior Lecturer in Statistics at the University of Kent. She has written and co-authored 15 papers on parameter redundancy and identifiability, including general theory and ecological applications.
Inhaltsangabe
1. Introduction 2. Problems With Parameter Redundancy 3. Parameter Redundancy and Identifiability Definitions and Theory 4. Practical General Methods for Detecting Parameter Redundancy and Identifiability 5. Detecting Parameter Redundancy and Identifiability in Complex Models 6. Bayesian Identifiability 7. Identifiability in Continuous State-Space Models 8. Identifiability in Discrete State-Space Models 9. Detecting Parameter Redundancy in Ecological Models 10. Concluding Remarks Appendix A. Maple Code Appendix B. Winbugs and R Code
1. Introduction 2. Problems With Parameter Redundancy 3. Parameter Redundancy and Identifiability Definitions and Theory 4. Practical General Methods for Detecting Parameter Redundancy and Identifiability 5. Detecting Parameter Redundancy and Identifiability in Complex Models 6. Bayesian Identifiability 7. Identifiability in Continuous State-Space Models 8. Identifiability in Discrete State-Space Models 9. Detecting Parameter Redundancy in Ecological Models 10. Concluding Remarks Appendix A. Maple Code Appendix B. Winbugs and R Code
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