mlsanityData to 14 Oct 2026

pgmpy: A Python Toolkit for Bayesian Networks

arXiv 2304.08639 ↗First posted 17 Apr 2023cs.LG stat.MEcited by 23

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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.

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

9 of 40 references.

References · 9

  1. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  2. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 41
  3. 2013Theory Refinement on Bayesian Networkscited by 28
  4. 2022Reasoning with Scene Graphs for Robot Planning under Partial Observabilitycited by 25
  5. 2020A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Futurecited by 22
  6. 2017Pomegranate: fast and flexible probabilistic modeling in pythoncited by 17
  7. 2016An Empirical-Bayes Score for Discrete Bayesian Networkscited by 9
  8. 2014BayesPy: Variational Bayesian Inference in Pythoncited by 4
  9. 2022A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Datacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 6; 2026: 15.

23 papers.

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  1. 2026When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMscited by 10
  2. 2023Equilibrium in the Computing Continuum through Active Inferencecited by 9
  3. 2024Adaptive Stream Processing on Edge Devices through Active Inferencecited by 6
  4. 2025Bayesian Network Structure Discovery Using Large Language Modelscited by 5
  5. 2026FunFact: Building Probabilistic Functional 3D Scene Graphs via Factor-Graph Reasoningcited by 5
  6. 2026Can Revealed Preferences Clarify LLM Alignment and Steering?cited by 5
  7. 2025On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Modelscited by 3
  8. 2026Dynamic Expert-Guided Model Averaging for Causal Discoverycited by 2
  9. 2025How Well Does Your Tabular Generator Learn the Structure of Tabular Data?cited by 1
  10. 2025Towards Probabilistic Question Answering Over Tabular Datacited by 1
  11. 2025A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech datacited by 1
  12. 2026CauTion: Knowing When to Trust LLMs for Ensemble Causal Discoverycited by 1
11 more
  1. 2025A Framework for Causal Concept-based Model Explanationscited by 0
  2. 2026DMCD: Semantic-Statistical Framework for Causal Discoverycited by 0
  3. 2026Automatic Causal Fairness Analysis with LLM-Generated Reportingcited by 0
  4. 2026A Bayesian Approach to Membership Inference for Statistical Releasecited by 0
  5. 2026Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Samplingcited by 0
  6. 2026A Neuroimaging Simulation Framework for Developing and Evaluating Causal AIcited by 0
  7. 2026A Filtered Mixture-of-Generators for Fully Synthetic Survival Trainingcited by 0
  8. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0
  9. 2026From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Datacited by 0
  10. 2026Conditional Independence Tests for Constraint-Based Causal Discovery: A Surveycited by 0
  11. 2026Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertaintycited by 0