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

arXiv: https://arxiv.org/abs/2105.00488 · HTML: https://mlsanity.com/paper/2105.00488/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 5 of 5)

- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions](https://mlsanity.com/paper/1301.6697/) (2013)
- [Partition MCMC for inference on acyclic digraphs](https://mlsanity.com/paper/1504.05006/) (2015)
- [Efficient Sampling and Structure Learning of Bayesian Networks](https://mlsanity.com/paper/1803.07859/) (2018)
- [Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains](https://mlsanity.com/paper/1302.4957/) (2013)

## Built on by (most cited first, 17 of 17)

- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Large Language Models for Constrained-Based Causal Discovery](https://mlsanity.com/paper/2406.07378/) (2024)
- [Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms](https://mlsanity.com/paper/2107.03863/) (2021)
- [Open problems in causal structure learning: A case study of COVID-19 in the UK](https://mlsanity.com/paper/2305.03859/) (2023)
- [Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Method](https://mlsanity.com/paper/2405.13100/) (2024)
- [The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions](https://mlsanity.com/paper/2205.02602/) (2022)
- [A Bayesian Take on Gaussian Process Networks](https://mlsanity.com/paper/2306.11380/) (2023)
- [Structure Learning with Adaptive Random Neighborhood Informed MCMC](https://mlsanity.com/paper/2311.00599/) (2023)
- [Bayesian Causal Inference with Gaussian Process Networks](https://mlsanity.com/paper/2402.00623/) (2024)
- [BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs](https://mlsanity.com/paper/2201.12003/) (2022)
- [BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes](https://mlsanity.com/paper/2208.10312/) (2022)
- [Tuning structure learning algorithms with out-of-sample and resampling strategies](https://mlsanity.com/paper/2306.13932/) (2023)
- [Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth](https://mlsanity.com/paper/2409.20187/) (2024)
- [Optimal Particle-based Approximation of Discrete Distributions (OPAD)](https://mlsanity.com/paper/2412.00545/) (2024)
- [High-Dimensional Inference in Bayesian Networks](https://mlsanity.com/paper/2112.09217/) (2021)
- [Exact discovery is polynomial for certain sparse causal Bayesian networks](https://mlsanity.com/paper/2406.15012/) (2024)
- [Scaling Up Bayesian DAG Sampling](https://mlsanity.com/paper/2510.25254/) (2025)

## 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.