Non-Destructive Sample Generation From Conditional Belief Functions
Artificial Intelligence
2020-05-26 v1
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.
Keywords
Cite
@article{arxiv.2005.11963,
title = {Non-Destructive Sample Generation From Conditional Belief Functions},
author = {Mieczysław A. Kłopotek},
journal= {arXiv preprint arXiv:2005.11963},
year = {2020}
}