mlsanityData to 7 Oct 2026

Learning Gaussian Networks

arXiv 1302.6808 ↗First posted 27 Feb 2013cs.AI cs.LG stat.MLcited by 62

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Abstract (arXiv)

We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Previous work has concentrated on metrics for domains containing only discrete variables, under the assumption that data represents a multinomial sample. In this paper, we extend this work, developing scoring metrics for domains containing all continuous variables or a mixture of discrete and continuous variables, under the assumption that continuous data is sampled from a multivariate normal distribution. Our work extends traditional statistical approaches for identifying vanishing regression coefficients in that we identify two important assumptions, called event equivalence and parameter modularity, that when combined allow the construction of prior distributions for multivariate normal parameters from a single prior Bayesian network specified by a user.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2013.

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Citing papers per year: 2021: 9; 2022: 17; 2023: 15; 2024: 7; 2025: 11; 2026: 3.

62 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  3. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  4. 2022GFlowNets and variational inferencecited by 103
  5. 2022Amortized Inference for Causal Structure Learningcited by 89
  6. 2021A survey of Bayesian Network structure learningcited by 85
  7. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  8. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  9. 2022From Statistical to Causal Learningcited by 40
  10. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 40
  11. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  12. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
38 more
  1. 2023Causal Representation Learning Made Identifiable by Grouping of Observational Variablescited by 30
  2. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  3. 2023Causal Modeling with Stationary Diffusionscited by 19
  4. 2024Recursive Causal Discoverycited by 12
  5. 2021Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithmscited by 11
  6. 2022GFlowCausal: Generative Flow Networks for Causal Discoverycited by 11
  7. 2023DAG Learning on the Permutahedroncited by 9
  8. 2024bnRep: A repository of Bayesian networks from the academic literaturecited by 9
  9. 2023Bayesian Structure Scores for Probabilistic Circuitscited by 7
  10. 2021Semiparametric Bayesian Networkscited by 6
  11. 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
  12. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  13. 2022Learning Latent Structural Causal Modelscited by 5
  14. 2022Directed Acyclic Graph Structure Learning from Dynamic Graphscited by 5
  15. 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
  16. 2022Domain Knowledge in A*-Based Causal Discoverycited by 4
  17. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  18. 2023Structure Learning with Adaptive Random Neighborhood Informed MCMCcited by 4
  19. 2025Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarkscited by 4
  20. 2021MIxBN: library for learning Bayesian networks from mixed datacited by 3
  21. 2022On the Sparse DAG Structure Learning Based on Adaptive Lassocited by 3
  22. 2023Shadow Datasets, New challenging datasets for Causal Representation Learningcited by 3
  23. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  24. 2024Optimal Kernel Choice for Score Function-based Causal Discoverycited by 3
  25. 2022Reframed GES with a Neural Conditional Dependence Measurecited by 2
  26. 2025gfnx: Fast and Scalable Library for Generative Flow Networks in JAXcited by 2
  27. 2022Learning Multitask Gaussian Bayesian Networkscited by 1
  28. 2022Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Setscited by 1
  29. 2022Latent Variable Models for Bayesian Causal Discoverycited by 1
  30. 2024Score-matching-based Structure Learning for Temporal Data on Networkscited by 1
  31. 2024Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexitycited by 1
  32. 2025Causal Discovery via Bayesian Optimizationcited by 1
  33. 2025DODO: Causal Structure Learning with Budgeted Interventionscited by 1
  34. 2025Towards the Formalization of a Trustworthy AI for Mining Interpretable Models explOiting Sophisticated Algorithmscited by 1
  35. 2026Information-Geometric Forward Policy Training in GFlowNetscited by 1
  36. 2021Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 0
  37. 2021Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Knowncited by 0
  38. 2021Likelihoods and Parameter Priors for Bayesian Networkscited by 0

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