Statistical inference with belief functions: A survey
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915993897402368 |
|---|---|
| author | Cuzzolin, Fabio |
| author_facet | Cuzzolin, Fabio |
| contents | Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a probability distribution for the problem impractical. The first step in a reasoning chain based on belief functions is inference: how to learn a belief measure from the available data. In this survey we focus, in particular, on making inference from statistical data, and review the most significant contributions in the area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07908 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Statistical inference with belief functions: A survey Cuzzolin, Fabio Statistics Theory Artificial Intelligence Machine Learning Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a probability distribution for the problem impractical. The first step in a reasoning chain based on belief functions is inference: how to learn a belief measure from the available data. In this survey we focus, in particular, on making inference from statistical data, and review the most significant contributions in the area. |
| title | Statistical inference with belief functions: A survey |
| topic | Statistics Theory Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.07908 |