Evidential Reasoning with Conditional Belief Functions

02/27/2013
by   Hong Xu, et al.
0

In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such networks. By analyzing the properties of some special evidential networks with conditional belief functions, we show that the reasoning process can be simplified in such kinds of networks.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 8

05/25/2020

Non-Destructive Sample Generation From Conditional Belief Functions

This paper presents a new approach to generate samples from conditional ...
03/27/2013

Default Reasoning and the Transferable Belief Model

Inappropriate use of Dempster's rule of combination has led some authors...
10/19/2012

A Linear Belief Function Approach to Portfolio Evaluation

By elaborating on the notion of linear belief functions (Dempster 1990; ...
02/13/2013

Network Engineering for Complex Belief Networks

Like any large system development effort, the construction of a complex ...
06/22/2021

Knowledge from Probability

We give a probabilistic analysis of inductive knowledge and belief and e...
03/31/2016

Ordinal Conditional Functions for Nearly Counterfactual Revision

We are interested in belief revision involving conditional statements wh...
03/27/2013

Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases

Kutato is a system that takes as input a database of cases and produces ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.