Towards conservative inference in credal networks using belief functions: the case of credal chains
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918088255995904 |
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| author | Sangalli, Marco Krak, Thomas de Campos, Cassio |
| author_facet | Sangalli, Marco Krak, Thomas de Campos, Cassio |
| contents | This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through a subclass of credal networks, namely chains. The proposed approach efficiently yields conservative intervals through belief and plausibility functions, combining computational speed with robust uncertainty representation. Key contributions include formalizing belief-based inference methods and comparing belief-based inference against classical sensitivity analysis. Numerical results highlight the advantages and limitations of applying belief inference within this framework, providing insights into its practical utility for chains and for credal networks in general. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards conservative inference in credal networks using belief functions: the case of credal chains Sangalli, Marco Krak, Thomas de Campos, Cassio Artificial Intelligence Probability This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through a subclass of credal networks, namely chains. The proposed approach efficiently yields conservative intervals through belief and plausibility functions, combining computational speed with robust uncertainty representation. Key contributions include formalizing belief-based inference methods and comparing belief-based inference against classical sensitivity analysis. Numerical results highlight the advantages and limitations of applying belief inference within this framework, providing insights into its practical utility for chains and for credal networks in general. |
| title | Towards conservative inference in credal networks using belief functions: the case of credal chains |
| topic | Artificial Intelligence Probability |
| url | https://arxiv.org/abs/2507.07619 |