# Belief Evolution Network-based Probability Transformation and Fusion

arXiv: https://arxiv.org/abs/2110.03468 · HTML: https://mlsanity.com/paper/2110.03468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 65 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Expert Opinion Extraction from a Biomedical Database](https://mlsanity.com/paper/1709.03270/) (2017)
- [A correlation coefficient of belief functions](https://mlsanity.com/paper/1612.05497/) (2016)
- [Pignistic Probability Transforms for Mixes of Low- and High-Probability Events](https://mlsanity.com/paper/1505.07751/) (2015)

## Built on by (most cited first, 3 of 3)

- [Random Graph Set and Evidence Pattern Reasoning Model](https://mlsanity.com/paper/2402.13058/) (2024)
- [Isopignistic Canonical Decomposition via Belief Evolution Network](https://mlsanity.com/paper/2405.02653/) (2024)
- [Evidential Information Fusion on Possibilistic Structure](https://mlsanity.com/paper/2605.17038/) (2026)

## Abstract (arXiv)

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence.