Introduction to WinBUGS for Ecologists

Bayesian statistics has exploded into biology and its sub-disciplines such as ecology over the years. This title offers a guide to applying WinBUGS to the types of models that the ecologists use, such as: linear (LM), generalized linear (GLM), linear mixed (LMM) and generalized linear mixed models (GLMM).

Autorentext
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences.

Klappentext

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS and its open-source sister OpenBugs is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Introduction to WINBUGS for Ecologists goes right to the heart of the matter by providing ecologists with a comprehensive, yet concise, guide to applying WinBUGS to the types of models that they use most often: linear (LM), generalized linear (GLM), linear mixed (LMM) and generalized linear mixed models (GLMM). Introduction to WinBUGS for Ecologists combines the use of simulated data sets "paired" analyses using WinBUGS (in a Bayesian framework for analysis) and in R (in a frequentist mode of inference) and uses a very detailed step-by-step tutorial presentation style that really lets the reader repeat every step of the application of a given mode in their own research.

Zusammenfassung
Combines the use of simulated data sets "paired" analyses using WinBUGS (in a Bayesian framework for analysis) and in R (in a frequentist mode of inference) and uses a very detailed step-by-step tutorial presentation style that really lets the reader repeat every step of the application of a given mode in their own research.

Inhalt

1. Introduction
   
   2. Principles of Bayesian Statistics
   3. WinBUGS
   4. A First Session in WinBUGS
   5. Running WinBUGS from R via R2WinBUGS
   6. Key Components of Generalized Linear Models
   7. T-Test, Normal Linear Regression
   8. Normal One-Way ANOVA
   9. Interaction, General Linear Model
   10. Linear Mixed-Effects Model
   11. Introduction to the Generalized Linear Model (GLM)
   12. Overdispersion and Offsets in the GLM
   13. Poisson ANCOVA
   14. Poisson Mixed-Effects Model
   15. Binomial T-Test
   16. Binomial ANCOVA
   17. Binomial Mixed-Effects Model
   18. Non-Standard GLMMs 1
   19. Non-Standard GLMMs 2
   20. Conclusion and Outlook
       Acknowledgements
       References