Practical Uses of Belief Functions

01/23/2013
by   Philippe Smets, et al.
0

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.

READ FULL TEXT

page 2

page 3

page 4

page 5

page 6

page 8

page 9

page 10

research
03/17/2015

Combining partially independent belief functions

The theory of belief functions manages uncertainty and also proposes a s...
research
10/19/2012

Decision Making with Partially Consonant Belief Functions

This paper studies decision making for Walley's partially consonant beli...
research
02/27/2013

Belief Maintenance in Bayesian Networks

Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning u...
research
09/18/2014

Belief revision by examples

A common assumption in belief revision is that the reliability of the in...
research
03/06/2013

Partially Specified Belief Functions

This paper presents a procedure to determine a complete belief function ...
research
10/05/2022

New results of 0-APN power functions over 𝔽_2^n

Partially APN functions attract researchers' particular interest recentl...
research
03/13/2013

Sensor Validation Using Dynamic Belief Networks

The trajectory of a robot is monitored in a restricted dynamic environme...

Please sign up or login with your details

Forgot password? Click here to reset