Projects per year
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
Original language | English |
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Title of host publication | Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022) |
Subtitle of host publication | Uncertainty in Artificial Intelligence, 1-5 August 2022, Eindhoven, The Netherlands |
Pages | 1085–1095 |
Publication status | Published - 2022 |
Event | 38th Conference on Uncertainty in Artificial Intelligence: UAI 2022 - Eindhoven, Netherlands Duration: 1 Aug 2022 → 5 Aug 2022 https://www.auai.org/uai2022/ |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 180 |
Conference
Conference | 38th Conference on Uncertainty in Artificial Intelligence |
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Abbreviated title | UAI 2022 |
Country/Territory | Netherlands |
City | Eindhoven |
Period | 1/08/22 → 5/08/22 |
Internet address |
Fields of Expertise
- Information, Communication & Computing
Fingerprint
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- 1 Finished
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Inference - General Framework for Inference on Graphical Models
1/10/20 → 30/09/22
Project: Research project