Jeffrey's rule of conditioning generalized to belief functions

03/06/2013
by   Philippe Smets, et al.
0

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rule of conditioning, respectively.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

research
06/08/2017

What Does a Belief Function Believe In ?

The conditioning in the Dempster-Shafer Theory of Evidence has been defi...
research
03/27/2013

Updating with Belief Functions, Ordinal Conditioning Functions and Possibility Measures

This paper discusses how a measure of uncertainty representing a state o...
research
04/21/2021

A geometric approach to conditioning belief functions

Conditioning is crucial in applied science when inference involving time...
research
02/27/2013

Possibilistic Conditioning and Propagation

We give an axiomatization of confidence transfer - a known conditioning ...
research
12/28/2021

Learning from What's Right and Learning from What's Wrong

The concept of updating (or conditioning or revising) a probability dist...
research
05/03/2018

NFL Injuries Before and After the 2011 Collective Bargaining Agreement (CBA)

The National Football League's (NFL) 2011 collective bargaining agreemen...
research
02/13/2013

Belief Revision with Uncertain Inputs in the Possibilistic Setting

This paper discusses belief revision under uncertain inputs in the frame...

Please sign up or login with your details

Forgot password? Click here to reset