Inclusion within Continuous Belief Functions
This work addresses a theoretical problem in uncertainty modeling for researchers in belief function theory, but it appears incremental as it builds on existing concepts without broad application.
The paper tackles the problem of defining and modeling inclusion relations for continuous belief functions, proposing strict and partial forms of inclusion and analyzing them through simulations with normal distributions to identify influencing parameters.
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.