# MMGET: A Markov model for generalized evidence theory

arXiv: https://arxiv.org/abs/2105.07952 · HTML: https://mlsanity.com/paper/2105.07952/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 69 references that are in the map.
- First posted on arXiv 12 May 2021.

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

- [Generalized Evidence Theory](https://mlsanity.com/paper/1404.4801/) (2014)
- [A total uncertainty measure for D numbers based on belief intervals](https://mlsanity.com/paper/1801.00702/) (2017)
- [On the negation of a Dempster-Shafer belief structure based on maximum uncertainty allocation](https://mlsanity.com/paper/1901.10072/) (2019)

## Built on by (most cited first, 7 of 7)

- [Limit of the Maximum Random Permutation Set Entropy](https://mlsanity.com/paper/2403.06206/) (2024)
- [Complementarity-driven Representation Learning for Multi-modal Knowledge Graph Completion](https://mlsanity.com/paper/2507.20620/) (2025)
- [Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.14343/) (2025)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [Co-Evidential Fusion with Information Volume for Medical Image Segmentation](https://mlsanity.com/paper/2506.02492/) (2025)
- [DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2507.19418/) (2025)
- [Adaptive Fuzzy Time Series Forecasting via Partially Asymmetric Convolution and Sub-Sliding Window Fusion](https://mlsanity.com/paper/2507.20641/) (2025)

## Abstract (arXiv)

In real life, lots of information merges from time to time. To appropriately describe the actual situations, lots of theories have been proposed. Among them, Dempster-Shafer evidence theory is a very useful tool in managing uncertain information. To better adapt to complex situations of open world, a generalized evidence theory is designed. However, everything occurs in sequence and owns some underlying relationships with each other. In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided. Besides, some numerical examples is offered to verify the correctness and rationality of the proposed method.