Non-Destructive Sample Generation From Conditional Belief Functions

TL;DR

This paper introduces a novel method for generating samples from conditional belief functions by leveraging Bayesian network structures, enabling non-destructive sampling for a specific subset of belief functions.

Contribution

It proposes a new approach to sample from conditional belief functions using belief function factorization along Bayesian networks, expanding sampling capabilities.

Findings

Effective sample generation for a subset of belief functions

Utilizes belief function decomposition along Bayesian networks

Enables non-destructive sampling methods

Abstract

This paper presents a new approach to generate samples from conditional belief functions for a restricted but non trivial subset of conditional belief functions. It assumes the factorization (decomposition) of a belief function along a bayesian network structure. It applies general conditional belief functions.