Why environmental scientists are becoming Bayesians
Advances in computational statistics provide a general framework for the high-dimensional models typically needed for ecological inference and prediction. Hierarchical Bayes (HB) represents a modelling structure with capacity to exploit diverse sources of information, to accommodate influences that are unknown (or unknowable), and to draw inference on large numbers of latent variables and parameters that describe complex relationships. Here I summarize the structure of HB and provide examples for common spatiotemporal problems. The flexible framework means that parameters, variables and latent variables can represent broader classes of model elements than are treated in traditional models. Inference and prediction depend on two types of stochasticity, including (1) uncertainty, which describes our knowledge of fixed quantities, it applies to all 'unobservables' (latent variables and parameters), and it declines asymptotically with sample size, and (2) variability, which applies to fluctuations that are not explained by deterministic processes and does not decline asymptotically with sample size. Examples demonstrate how different sources of stochasticity impact inference and prediction and how allowance for stochastic influences can guide research.
Duke Scholars
Altmetric Attention Stats
Dimensions Citation Stats
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Ecology
- 4104 Environmental management
- 4102 Ecological applications
- 3103 Ecology
- 0603 Evolutionary Biology
- 0602 Ecology
- 0501 Ecological Applications
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Ecology
- 4104 Environmental management
- 4102 Ecological applications
- 3103 Ecology
- 0603 Evolutionary Biology
- 0602 Ecology
- 0501 Ecological Applications