Quantifying Uncertainty: Modern Computational Representation of Probability and Applications

This is a link to a pdf file containing a tutorial on modeling uncertainty:

http://www.wire.tu-bs.de/forschung/talks/06_Opatija.pdf

Many descriptions (especially of future events) contain
elements, which are uncertain and not precisely known.

• For example future rainfall, or discharge from a river.

• More generally, action from surrounding environment.

• The system itself may contain only incompletely known

• parameters, processes or fields (not possible or too

• costly to measure)

• There may be small, unresolved scales in the model,

• they act as a kind of background noise.

All these introduce some uncertainty in the model.

• Uncertainty may be aleatoric, which means random and not reducible, or

• epistemic, which means due to incomplete knowledge.