# Theory Refinement on Bayesian Networks

arXiv: https://arxiv.org/abs/1303.5709 · HTML: https://mlsanity.com/paper/1303.5709/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2013.

## Built on by (most cited first, 28 of 28)

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- [Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine](https://mlsanity.com/paper/2110.07785/) (2021)
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- [Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks](https://mlsanity.com/paper/2406.17518/) (2024)
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- [Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures](https://mlsanity.com/paper/2408.14935/) (2024)
- [QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs](https://mlsanity.com/paper/2410.23155/) (2024)
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- [Exact Learning Augmented Naive Bayes Classifier](https://mlsanity.com/paper/2107.03018/) (2021)
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- [Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization](https://mlsanity.com/paper/2505.11089/) (2025)
- [Scaling Up Bayesian DAG Sampling](https://mlsanity.com/paper/2510.25254/) (2025)
- [Causal Discovery in the Era of Agents](https://mlsanity.com/paper/2606.23608/) (2026)
- [Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals](https://mlsanity.com/paper/2608.21079/) (2026)
- [xWhyL: Causal Interactive Learning](https://mlsanity.com/paper/2609.26037/) (2026)

## 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.