mlsanityData to 7 Oct 2026

Structure Learning in Graphical Modeling

arXiv 1606.02359 ↗First posted 7 Jun 2016stat.ME stat.MLcited by 66

Not on the map. Why?

Abstract (arXiv)

A graphical model is a statistical model that is associated to a graph whose nodes correspond to variables of interest. The edges of the graph reflect allowed conditional dependencies among the variables. Graphical models admit computationally convenient factorization properties and have long been a valuable tool for tractable modeling of multivariate distributions. More recently, applications such as reconstructing gene regulatory networks from gene expression data have driven major advances in structure learning, that is, estimating the graph underlying a model. We review some of these advances and discuss methods such as the graphical lasso and neighborhood selection for undirected graphical models (or Markov random fields), and the PC algorithm and score-based search methods for directed graphical models (or Bayesian networks). We further review extensions that account for effects of latent variables and heterogeneous data sources.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 13; 2023: 9; 2024: 14; 2025: 8; 2026: 8.

66 papers.

Sort:

By area:

  1. 2022Learning to Induce Causal Structurecited by 68
  2. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  3. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  4. 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
  5. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  6. 2021Bayesian structure learning and sampling of Bayesian networks with the R package BiDAGcited by 17
  7. 2021Learning non-Gaussian graphical models via Hessian scores and triangular transportcited by 16
  8. 2022Distributed Learning of Generalized Linear Causal Networkscited by 13
  9. 2023Structured Neural Networks for Density Estimation and Causal Inferencecited by 13
  10. 2023The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligencecited by 12
  11. 2021Joint Gaussian Graphical Model Estimation: A Surveycited by 10
  12. 2022Valid Inference After Causal Discoverycited by 10
38 more
  1. 2022Ensemble transport smoothing. Part II: Nonlinear updatescited by 8
  2. 2022Learning Linear Non-Gaussian Polytree Modelscited by 7
  3. 2023Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunitiescited by 7
  4. 2021Estimating a Directed Tree for Extremescited by 5
  5. 2021Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernelcited by 5
  6. 2022Learning Graph Structure from Convolutional Mixturescited by 5
  7. 2024Parameter identification in linear non-Gaussian causal models under general confoundingcited by 5
  8. 2025Localized Diffusion Modelscited by 5
  9. 2023Structure Learning with Adaptive Random Neighborhood Informed MCMCcited by 4
  10. 2024Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Modelscited by 4
  11. 2025A friendly introduction to triangular transportcited by 4
  12. 2021Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networkscited by 3
  13. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  14. 2024Multiview Graph Learning with Consensus Graphcited by 3
  15. 2025Nonlinear Causal Discovery for Grouped Datacited by 3
  16. 2021Prequential MDL for Causal Structure Learning with Neural Networkscited by 2
  17. 2022CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priorscited by 2
  18. 2024Scalable network reconstruction in subquadratic timecited by 2
  19. 2024Greedy equivalence search for nonparametric graphical modelscited by 2
  20. 2024High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matchingcited by 2
  21. 2024Discrete distributions are learnable from metastable samplescited by 2
  22. 2021How do some Bayesian Network machine learned graphs compare to causal knowledge?cited by 1
  23. 2021Gaussian Experts Selection using Graphical Modelscited by 1
  24. 2021Learning Linear Polytree Structural Equation Modelscited by 1
  25. 2021Fast and Accurate Graph Learning for Huge Data via Minipatch Ensemblescited by 1
  26. 2022Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lenscited by 1
  27. 2023Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networkscited by 1
  28. 2024Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamicscited by 1
  29. 2025Probabilistic and Causal Satisfiability: Constraining the Modelcited by 1
  30. 2025Consistent Bayesian causal discovery for structural equation models with equal error variancescited by 1
  31. 2021WiseR: An end-to-end structure learning and deployment framework for causal graphical modelscited by 0
  32. 2021Interaction Models and Generalized Score Matching for Compositional Datacited by 0
  33. 2021Decentralized Learning of Tree-Structured Gaussian Graphical Models from Noisy Datacited by 0
  34. 2022Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processescited by 0
  35. 2022pyGSL: A Graph Structure Learning Toolkitcited by 0
  36. 2022Robust Model Selection of Gaussian Graphical Modelscited by 0
  37. 2023Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithmcited by 0
  38. 2023Distributionally Robust Skeleton Learning of Discrete Bayesian Networkscited by 0

50 most cited of 66 All 66 (JSON)