Bayesian Population Analysis using WinBUGS: A hierarchical perspective by Marc KeryBayesian Population Analysis using WinBUGS: A hierarchical perspective by Marc Kery

Bayesian Population Analysis using WinBUGS: A hierarchical perspective

byMarc Kery, Michael SchaubEditorMarc Kery

Paperback | September 28, 2011

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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.

  • Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist
  • All WinBUGS/OpenBUGS analyses are completely integrated in software R
  • Includes complete documentation ofallR and WinBUGS code required to conduct analyses and showsallthe necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R
Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of Zürich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e.,...
Title:Bayesian Population Analysis using WinBUGS: A hierarchical perspectiveFormat:PaperbackDimensions:554 pages, 9 × 6 × 0.68 inPublished:September 28, 2011Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0123870208

ISBN - 13:9780123870209

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Table of Contents

1. Introduction

2. Very brief introduction to Bayesian statistical modeling

3. Introduction to the generalized linear model (GLM): The simplest model for count data

4. Introduction to random effects: The conventional Poisson GLMM for count data

5. State-space models

6. Estimation of population size

7. Estimation of survival probabilities using capture-recapture data

8. Estimation of survival probabilities using mark-recovery data

9. Multistate capture-recapture models

10. Estimation of survival and recruitment using the Jolly-Seber model

11. Integrated population models

12. Metapopulation modeling of abundance using hierarchical Poisson regression

13. Metapopulation modeling of species distributions using hierarchical logistic regression

14. Concluding remarks