Practical Uses of Belief Functions
  We present examples where the use of belief functions provided sound and
elegant solutions to real life problems. These are essentially characterized by
?missing' information. The examples deal with 1) discriminant analysis using a
learning set where classes are only partially known; 2) an information
retrieval systems handling inter-documents relationships; 3) the combination of
data from sensors competent on partially overlapping frames; 4) the
determination of the number of sources in a multi-sensor environment by
studying the inter-sensors contradiction. The purpose of the paper is to report
on such applications where the use of belief functions provides a convenient
tool to handle ?messy' data problems.
