Inclusion within Continuous Belief Functions

TL;DR

This paper introduces two forms of inclusion for continuous belief functions, analyzes their properties using normal distributions, and identifies key parameters influencing these inclusion relations.

Contribution

It proposes and analyzes strict and partial inclusion relations for continuous belief functions, focusing on consonant belief functions and their parameter influences.

Findings

Defined strict and partial inclusion forms

Simulated normal distributions to analyze inclusion relations

Identified parameters influencing inclusion

Abstract

Defining and modeling the relation of inclusion between continuous belief function may be considered as an important operation in order to study their behaviors. Within this paper we will propose and present two forms of inclusion: The strict and the partial one. In order to develop this relation, we will study the case of consonant belief function. To do so, we will simulate normal distributions allowing us to model and analyze these relations. Based on that, we will determine the parameters influencing and characterizing the two forms of inclusion.