Marc Kéry,
Michael Schaub
Bayesian Population Analysis using WinBUGS
A Hierarchical Perspective
Paperback Engels 2011 9780123870209Levertijd ± 9 werkdagen
Gratis verzonden
Samenvatting
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.
Specificaties
Lezersrecensies
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Inhoudsopgave
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
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
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