# pgmpy: A Python Toolkit for Bayesian Networks

arXiv: https://arxiv.org/abs/2304.08639 · HTML: https://mlsanity.com/paper/2304.08639/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [A fast PC algorithm for high dimensional causal discovery with multi-core PCs](https://mlsanity.com/paper/1502.02454/) (2015)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [Theory Refinement on Bayesian Networks](https://mlsanity.com/paper/1303.5709/) (2013)
- [Reasoning with Scene Graphs for Robot Planning under Partial Observability](https://mlsanity.com/paper/2202.10432/) (2022)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Pomegranate: fast and flexible probabilistic modeling in python](https://mlsanity.com/paper/1711.00137/) (2017)
- [An Empirical-Bayes Score for Discrete Bayesian Networks](https://mlsanity.com/paper/1605.03884/) (2016)
- [BayesPy: Variational Bayesian Inference in Python](https://mlsanity.com/paper/1410.0870/) (2014)
- [A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data](https://mlsanity.com/paper/2206.04356/) (2022)

## Built on by (most cited first, 23 of 23)

- [When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs](https://mlsanity.com/paper/2602.06286/) (2026)
- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Bayesian Network Structure Discovery Using Large Language Models](https://mlsanity.com/paper/2511.00574/) (2025)
- [FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoning](https://mlsanity.com/paper/2604.03696/) (2026)
- [Can Revealed Preferences Clarify LLM Alignment and Steering?](https://mlsanity.com/paper/2605.08556/) (2026)
- [On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models](https://mlsanity.com/paper/2502.08531/) (2025)
- [Dynamic Expert-Guided Model Averaging for Causal Discovery](https://mlsanity.com/paper/2601.16715/) (2026)
- [How Well Does Your Tabular Generator Learn the Structure of Tabular Data?](https://mlsanity.com/paper/2503.09453/) (2025)
- [Towards Probabilistic Question Answering Over Tabular Data](https://mlsanity.com/paper/2506.20747/) (2025)
- [A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data](https://mlsanity.com/paper/2512.07741/) (2025)
- [CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery](https://mlsanity.com/paper/2606.03602/) (2026)
- [A Framework for Causal Concept-based Model Explanations](https://mlsanity.com/paper/2512.02735/) (2025)
- [DMCD: Semantic-Statistical Framework for Causal Discovery](https://mlsanity.com/paper/2602.20333/) (2026)
- [Automatic Causal Fairness Analysis with LLM-Generated Reporting](https://mlsanity.com/paper/2604.27011/) (2026)
- [A Bayesian Approach to Membership Inference for Statistical Release](https://mlsanity.com/paper/2605.30203/) (2026)
- [Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Sampling](https://mlsanity.com/paper/2606.20878/) (2026)
- [A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI](https://mlsanity.com/paper/2606.28684/) (2026)
- [A Filtered Mixture-of-Generators for Fully Synthetic Survival Training](https://mlsanity.com/paper/2607.00127/) (2026)
- [Integrating Background Knowledge for Scalable Causal Discovery](https://mlsanity.com/paper/2607.10456/) (2026)
- [From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data](https://mlsanity.com/paper/2607.26521/) (2026)
- [Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey](https://mlsanity.com/paper/2608.11156/) (2026)
- [Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty](https://mlsanity.com/paper/2610.16902/) (2026)

## Abstract (arXiv)

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.