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SUMMARY:Validation of uncertain ecological models with imprecise data
DTSTART;VALUE=DATE:20140915
DTSTAMP:20260509T234213Z
UID:fa0e4e3d5686937b3f347e70173f66a55d4619f3cb6e581776b1dd63
CATEGORIES:Conferences - Seminars
DESCRIPTION:Scott Ferson\nThe predictive capability of ecological models\,
  which determines what we can reliably infer from them\, is assessed by wh
 ether and how closely the model can be shown to yield predictions conformi
 ng with available empirical observations beyond those data used in the mod
 el calibration process. Realistic ecological models usually incorporate st
 ochasticity to mimic the variability in the natural world\, which means th
 at their predictions are often expressed as probability distributions or s
 imilarly uncertain numbers. Validation of these models must also contend w
 ith data that are usually sparse and often imprecise. But this stochastici
 ty 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 predicti
 ve capability is degraded by both phenomena. One might hope to define a sc
 alar 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 conce
 rned with aleatory uncertainty. This workshop will investigate the appropr
 iate accounting that is needed for conducting proper validations and estim
 ating predictive capabilities of ecological models.
LOCATION:BI A0 448 https://plan.epfl.ch/?room==BI%20A0%20448
STATUS:CONFIRMED
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