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Abstract
Belief propagation is an iterative method for inference in probabilistic graphical models. Its well-known relationship to a classical concept from statistical physics, the Bethe free energy, puts it on a solid theoretical foundation. If belief propagation fails to approximate the marginals, then this is often due to a failure of the Bethe approximation. In this work, we show how modifications in a graphical model can be a great remedy for fixing the Bethe approximation. Specifically, we analyze how the removal of edges influences and improves belief propagation, and demonstrate that this positive effect is particularly distinct for dense graphs
Originalsprache | englisch |
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Titel | Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022) |
Untertitel | Uncertainty in Artificial Intelligence, 1-5 August 2022, Eindhoven, The Netherlands |
Seiten | 1085–1095 |
Publikationsstatus | Veröffentlicht - 2022 |
Veranstaltung | 38th Conference on Uncertainty in Artificial Intelligence: UAI 2022 - Eindhoven, Niederlande Dauer: 1 Aug. 2022 → 5 Aug. 2022 https://www.auai.org/uai2022/ |
Publikationsreihe
Name | Proceedings of Machine Learning Research |
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Band | 180 |
Konferenz
Konferenz | 38th Conference on Uncertainty in Artificial Intelligence |
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Kurztitel | UAI 2022 |
Land/Gebiet | Niederlande |
Ort | Eindhoven |
Zeitraum | 1/08/22 → 5/08/22 |
Internetadresse |
Fields of Expertise
- Information, Communication & Computing
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Inference - Allgemeiner Rahmen für Interferenzen auf graphischen Modellen
1/10/20 → 30/09/22
Projekt: Forschungsprojekt