Epistemic uncertainty is uncertainty due to lack of knowledge.

In %Dakota, epistemic uncertainty can be characterized by interval- or
set-valued variables (see relevant keywords below) that are propagated
to calculate bounding intervals on simulation output using interval
analysis methods.  These epistemic variable types can optionally
include basic probability assignments for use in Dempster-Shafer
theory of evidence methods.  Epistemic uncertainty can alternately be
modeled with probability density functions, although results from UQ
studies are then typically interpreted as possibilities or bounds, as
opposed to a probability distribution of responses.

Through \ref model-nested "nested models", Dakota can perform combined
aleatory / epistemic analyses such as second-order probability or
probability of frequency.  For example, a variable can be assumed to
have a lognormal distribution with specified variance, with its mean
expressed as an epistemic uncertainty lying in an expert-specified
interval.
