**Uncertainty Quantification Working Group**

March 21, 11:30 AM, CNLS Conf. Room, TA-3, Bldg. 1690

## Novel Uncertainty Quantification Methods Based on Generalized Information Theory

Cliff Joslyn, Modeling, Algorithms, and Informatics, CCS-3

### Abstract

Researchers at the Los Alamos and Sandia National Laboratories are
exploring the utility of non-traditional methods of uncertainty
quantitifcaion for engineering modeling applications such as risk and
reliability analysis. Described broadly as Generalized Information
Theory (GIT), these approaches include random set theory,
Dempster-Shafer evidence theory, interval analysis and probability
intervals, possibility theory, and fuzzy systems, amongst others. Such
approaches are intended to complement and augment statistical,
Bayesian, and traditional probabilistic methods of uncertainty
quantification when input data are highly sparse, qualitative,
imprecise, or derived from non-quantitative sources such as linguistic
expressions.

A workshop held at Los Alamos at the end of February
(http://www.c3.lanl.gov/~joslyn/epistemic) provided a forum for the
LANL research community and members of Sandia's Epistemic Uncertainty
Project to share and discuss their technical approaches in this
area. In this talk we will first present a synoptic discussion of the
mathematical formalism of the components of GIT mentioned above, and
more importantly their formal relations. We will then briefly review a
number of the presentations from the workhsop and the work being done
for the ASCI program in the quantification of epistemic uncertainty in
large numerical simulations (http://www.sandia.gov/epistemic).

To send e-mail to author: joslyn@lanl.gov .
His web site is http://www.c3.lanl.gov/~joslyn/.