By Marc Kery, Michael Schaub
Bayesian data has exploded into biology and its sub-disciplines, resembling ecology, during the last decade. The unfastened software WinBUGS, and its open-source sister OpenBugs, is at present the one versatile and general-purpose application on hand with which the common ecologist can behavior ordinary and non-standard Bayesian statistics.
- Comprehensive and richly commented examples illustrate quite a lot of versions which are so much proper to the learn of a latest inhabitants ecologist
- All WinBUGS/OpenBUGS analyses are thoroughly built-in in software program R
- Includes entire documentation of all R and WinBUGS code required to behavior analyses and exhibits all the required steps from having the knowledge in a textual content dossier out of Excel to studying and processing the output from WinBUGS in R
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Additional resources for Bayesian Population Analysis using WinBUGS. A hierarchical perspective
Explicit hierarchical models have random variables or parameters with an explicit ecological interpretation, while implicit hierarchical models do not. As an example of an implicit hierarchical model, the Poisson GLMMs in Chapter 4 have a quantity called the expected count (λ). This is not a real ecological parameter because it is the product of population size and detection probability. In contrast, in the hierarchical models in Chapter 6, N is the sum of the latent indicator variables z and corresponds exactly to the local population size.
2002), but again, its standard version computed by WinBUGS appears to be problematic for hierarchical models—and most models that ecologists nowadays want to fit have more than one random component and therefore are mixed, or hierarchical, models (see Chapter 4). The DIC can be computed for such hierarchical models (see Millar, 2009, which includes R code), but the required computations are involved and computationally very demanding. Hence, in spite of long-standing criticisms of stepwise model selection and model selection by significance tests, one may effectively be back at one of those.
There are broadly two different objectives of modeling, and they may lead to two different modes of building a model: explanation and prediction. Explanation means understanding and will typically require simpler models than prediction (Caswell, 1988). The explanatory mode of modeling focuses on the actual model structure. It is hoped that the kind of parameters and their values have some relevance for how nature generated the observed output. The focus is more on the parameters θ. In contrast, prediction focuses on the system output, the response y, and thus aims at predicting the response as well as possible either within the sample studied or for the entire statistical population that is represented by the sample.