Validation of uncertain ecological models with imprecise data
Event details
| Date | 15.09.2014 › 19.09.2014 |
| Speaker | Scott Ferson |
| Location | |
| Category | Conferences - Seminars |
The predictive capability of ecological models, which determines what we can reliably infer from them, is assessed by whether and how closely the model can be shown to yield predictions conforming with available empirical observations beyond those data used in the model calibration process. Realistic ecological models usually incorporate stochasticity to mimic the variability in the natural world, which means that their predictions are often expressed as probability distributions or similarly uncertain numbers. Validation of these models must also contend with data that are usually sparse and often imprecise. But this stochasticity and imprecision complicate the validation process considerably. A match between the model and data can sometimes be easier to establish when the predictions are uncertain because of ambiguities about the model structure or when empirical measurements are imprecise, but the resulting predictive capability is degraded by both phenomena. One might hope to define a scalar metric that assesses in some overall sense the dissimilarity between predictions and observations. But there may be cases in which it would be more informative and useful to distinguish predictions and observations in two senses, say, one concerned with epistemic uncertainty and one concerned with aleatory uncertainty. This workshop will investigate the appropriate accounting that is needed for conducting proper validations and estimating predictive capabilities of ecological models.
Links
Practical information
- General public
- Free
Organizer
- CIB
Contact
- Isabelle Derivaz-Rabii