mlsanityData to 7 Oct 2026

Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG

arXiv 2105.00488 ↗First posted 2 May 2021stat.CO stat.MLcited by 17

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Abstract (arXiv)

The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize within the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 5 of its 39 references that are in the map. First posted on arXiv 2 May 2021.

Builds on

5 of 39 references.

References · 5

  1. 2016Structure Learning in Graphical Modelingcited by 66
  2. 2013Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 41
  3. 2015Partition MCMC for inference on acyclic digraphscited by 33
  4. 2018Efficient Sampling and Structure Learning of Bayesian Networkscited by 28
  5. 2013Learning Bayesian Networks: A Unification for Discrete and Gaussian Domainscited by 21

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Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 4; 2024: 6; 2025: 1.

17 papers.

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  1. 2021A survey of Bayesian Network structure learningcited by 85
  2. 2024Large Language Models for Constrained-Based Causal Discoverycited by 15
  3. 2021Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithmscited by 11
  4. 2023Open problems in causal structure learning: A case study of COVID-19 in the UKcited by 7
  5. 2024Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Methodcited by 6
  6. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4
  7. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  8. 2023Structure Learning with Adaptive Random Neighborhood Informed MCMCcited by 4
  9. 2024Bayesian Causal Inference with Gaussian Process Networkscited by 2
  10. 2022BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGscited by 1
  11. 2022BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodescited by 1
  12. 2023Tuning structure learning algorithms with out-of-sample and resampling strategiescited by 1
5 more
  1. 2024Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truthcited by 1
  2. 2021High-Dimensional Inference in Bayesian Networkscited by 0
  3. 2024Exact discovery is polynomial for certain sparse causal Bayesian networkscited by 0
  4. 2024Optimal Particle-based Approximation of Discrete Distributions (OPAD)cited by 0
  5. 2025Scaling Up Bayesian DAG Samplingcited by 0