Leveraging cluster backbones for improving MAP inference in statistical relational models

DOI10.1007/s10472-020-09698-zzbMath1473.68132OpenAlexW3021101329MaRDI QIDQ2202525

Gilles Pesant, Christopher J. Pal, Mohamed Hamza Ibrahim

Publication date: 18 September 2020

Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10472-020-09698-z

zbMATH Keywords

Markov logic networksurvey propagationmaximum-a-posteriori inference

Mathematics Subject Classification ID

General topics in artificial intelligence (68T01)

Uses Software

• Walksat
• ToulBar2
• DIMACS
• Alchemy
• GitHub
• Tuffy
• Xeggora
• RockIt
• SPFlow
• LoMRF

Cites Work

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• Learning first-order probabilistic models with combining rules
• Markov logic networks
• A new look at survey propagation and its generalizations
• MAP Estimation Via Agreement on Trees: Message-Passing and Linear Programming
• Relaxed Survey Propagation for The Weighted Maximum Satisfiability Problem
• Instability of one-step replica-symmetry-broken phase in satisfiability problems
• On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
• State-Space Abstractions for Probabilistic Inference: A Systematic Review
• Random Formulas Have Frozen Variables
• Xeggora: Exploiting Immune-to-Evidence Symmetries with Full Aggregation in Statistical Relational Models
• Survey propagation: An algorithm for satisfiability
• Theory and Applications of Satisfiability Testing
• Phase Transitions in Combinatorial Optimization Problems

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