mlsanityData to 7 Oct 2026

Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

arXiv 1302.4957 ↗First posted 20 Feb 2013cs.AIcited by 21

Not on the map. Why?

Abstract (arXiv)

We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 1; 2023: 8; 2024: 4; 2025: 1.

21 papers.

Sort:

  1. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  2. 2023Causal Order: The Key to Leveraging Imperfect Experts in Causal Inferencecited by 46
  3. 2021The impact of prior knowledge on causal structure learningcited by 35
  4. 2023Causal Structure Learning Supervised by Large Language Modelcited by 21
  5. 2021Bayesian structure learning and sampling of Bayesian networks with the R package BiDAGcited by 17
  6. 2024Large Language Models for Constrained-Based Causal Discoverycited by 15
  7. 2024bnRep: A repository of Bayesian networks from the academic literaturecited by 9
  8. 2023Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis resultscited by 5
  9. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4
  10. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  11. 2021Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networkscited by 3
  12. 2023Bayesian causal discovery from unknown general interventionscited by 2
9 more
  1. 2021Causal Markov Boundariescited by 1
  2. 2023Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Modelcited by 1
  3. 2025Less Greedy Equivalence Searchcited by 1
  4. 2021Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 0
  5. 2021Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Knowncited by 0
  6. 2021Likelihoods and Parameter Priors for Bayesian Networkscited by 0
  7. 2023Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clusteringcited by 0
  8. 2024Exact discovery is polynomial for certain sparse causal Bayesian networkscited by 0
  9. 2024Optimal Particle-based Approximation of Discrete Distributions (OPAD)cited by 0