**Date & Time:** January 24, 2013 :: 16:00-17:00

**Venue:** Ramanujan Hall

**Title:**The Dempster-Shafer Theory of Belief Functions for Managing
Uncertainties: An Introduction

**Speaker:** Prof. R. P. Srivastava
The University of Kansas, Lawrence, Kansas.

**Abstract:** The main purpose of the presentation is to introduce the Dempster-Shafer
(DS) theory of belief functions and illustrate its flexibility through
various applications. The DS theory was made popular by Shafer during
the 1970s through his book A Mathematical Theory of Evidence published
by Princeton University Press in 1976. Basically, the DS theory is a
generalization of the Bayesian formalism; Bayes rule is a special case
of [WINDOWS-1252?]Dempsterâ€™s rule. The Bayesian formalism makes direct probability
statements about questions of interest, whereas the DS theory brings the
probability statement on questions of interest in an indirect way. The
advantage of the DS theory over Bayesian framework is in its ability to
model ignorance explicitly. Evidence faced in the real world such as
medicine, legal domain, or business, inherently contains partial
ignorance. Thus, the DS theory is more appropriate for modeling and
managing uncertainties in such situations. In fact, currently, the DS
theory is being used in identifying objects and images in the real
world. Also, it is being used for developing expert systems and for
assessing risks such as fraud risk, information security risk, etc. The
presentation will highlight some of these applicat