mlsanityData to 7 Oct 2026

Theory Refinement on Bayesian Networks

arXiv 1303.5709 ↗First posted 20 Mar 2013cs.AIcited by 28

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Abstract (arXiv)

Theory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2013.

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Citing papers per year: 2021: 7; 2022: 0; 2023: 8; 2024: 6; 2025: 4; 2026: 3.

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  1. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  2. 2021A survey of Bayesian Network structure learningcited by 85
  3. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  4. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  5. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  6. 2023pgmpy: A Python Toolkit for Bayesian Networkscited by 21
  7. 2023Towards Practical Federated Causal Structure Learningcited by 9
  8. 2023Bayesian Structure Scores for Probabilistic Circuitscited by 7
  9. 2021Partitioned hybrid learning of Bayesian network structurescited by 4
  10. 2021Active Structure Learning of Bayesian Networks in an Observational Settingcited by 4
  11. 2024Learning Where to Edit Vision Transformerscited by 4
  12. 2021A Framework for Assurance of Medication Safety using Machine Learningcited by 3
16 more
  1. 2025CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulationscited by 3
  2. 2021Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicinecited by 2
  3. 2021Scalable Bayesian Network Structure Learning with Splinescited by 2
  4. 2024Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networkscited by 2
  5. 2023Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causalitycited by 1
  6. 2024Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structurescited by 1
  7. 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMscited by 1
  8. 2024Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexitycited by 1
  9. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 1
  10. 2021Exact Learning Augmented Naive Bayes Classifiercited by 0
  11. 2024An Efficient Procedure for Computing Bayesian Network Structure Learningcited by 0
  12. 2025Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimizationcited by 0
  13. 2025Scaling Up Bayesian DAG Samplingcited by 0
  14. 2026Causal Discovery in the Era of Agentscited by 0
  15. 2026Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposalscited by 0
  16. 2026xWhyL: Causal Interactive Learningcited by 0