mlsanityData to 7 Oct 2026

Bucket Elimination: A Unifying Framework for Several Probabilistic Inference

arXiv 1302.3572 ↗First posted 13 Feb 2013cs.AIcited by 13

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Abstract (arXiv)

Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination--type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's structure.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2013.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 1; 2024: 1; 2025: 1.

13 papers.

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  1. 2021VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decompositioncited by 25
  2. 2022Causal Inference Using Tractable Circuitscited by 12
  3. 2021Partition Function Estimation: A Quantitative Studycited by 8
  4. 2022On the Complexity of Counterfactual Reasoningcited by 7
  5. 2023Mixtures of All Treescited by 3
  6. 2024Identifying Causal Effects Under Functional Dependenciescited by 3
  7. 2021Pretrained Cost Model for Distributed Constraint Optimization Problemscited by 1
  8. 2022Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problemscited by 1
  9. 2021Faster Exact MPE and Constrained Optimization with Deterministic Finite State Automatacited by 0
  10. 2021Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representationscited by 0
  11. 2022Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysiscited by 0
  12. 2022Efficient End-to-End AutoML via Scalable Search Space Decompositioncited by 0
1 more
  1. 2025GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimizationcited by 0