# bnRep: A repository of Bayesian networks from the academic literature

arXiv: https://arxiv.org/abs/2409.19158 · HTML: https://mlsanity.com/paper/2409.19158/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Learning Gaussian Networks](https://mlsanity.com/paper/1302.6808/) (2013)
- [The impact of prior knowledge on causal structure learning](https://mlsanity.com/paper/2102.00473/) (2021)
- [Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data](https://mlsanity.com/paper/2005.09020/) (2020)
- [Efficient Sampling and Structure Learning of Bayesian Networks](https://mlsanity.com/paper/1803.07859/) (2018)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains](https://mlsanity.com/paper/1302.4957/) (2013)
- [BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning](https://mlsanity.com/paper/2003.01207/) (2020)
- [Greedy structure learning from data that contain systematic missing values](https://mlsanity.com/paper/2107.04184/) (2021)
- [Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package](https://mlsanity.com/paper/2107.11785/) (2021)
- [Semiparametric Bayesian Networks](https://mlsanity.com/paper/2109.03008/) (2021)
- [Learning Bayesian Networks from Incomplete Data with the Node-Average Likelihood](https://mlsanity.com/paper/2004.14441/) (2020)
- [From Science to Management: Using Bayesian Networks to Learn about Lyngbya](https://mlsanity.com/paper/1405.4692/) (2014)

## Built on by (most cited first, 9 of 9)

- [Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterization](https://mlsanity.com/paper/2505.15918/) (2025)
- [Bayesian Network Structure Discovery Using Large Language Models](https://mlsanity.com/paper/2511.00574/) (2025)
- [Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms](https://mlsanity.com/paper/2504.01740/) (2025)
- [Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models](https://mlsanity.com/paper/2602.10282/) (2026)
- [Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networks](https://mlsanity.com/paper/2509.22111/) (2025)
- [Structural Refinement of Bayesian Networks for Efficient Model Parameterisation](https://mlsanity.com/paper/2510.00334/) (2025)
- [Variance Computation for Weighted Model Counting with Knowledge Compilation Approach](https://mlsanity.com/paper/2601.03523/) (2026)
- [Structured Inference with Large Language Gibbs](https://mlsanity.com/paper/2606.19264/) (2026)
- [LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orientation](https://mlsanity.com/paper/2608.27472/) (2026)

## Abstract (arXiv)

Bayesian networks (BNs) are widely used for modeling complex systems with uncertainty, yet repositories of pre-built BNs remain limited. This paper introduces bnRep, an open-source R package offering a comprehensive collection of documented BNs, facilitating benchmarking, replicability, and education. With over 200 networks from academic publications, bnRep integrates seamlessly with bnlearn and other R packages, providing users with interactive tools for network exploration.