pgmpy
Python library for Probabilistic Graphical Models
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is pgmpy?
Pgmpy is a Python library that allows users to create and perform inference on probabilistic graphical models, including Bayesian networks and Markov models. It's essential for developers working with uncertainty in data.
Key differentiator
“Pgmpy stands out as a comprehensive Python library for working with Probabilistic Graphical Models, offering both flexibility and depth in model creation and inference.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Performance degrades significantly with an increase in the number of nodes and edges, making it impractical for real-time or big data applications
The official documentation lacks comprehensive tutorials and practical examples, leading to a steep learning curve for new users
Frequent changes in the API can lead to breaking changes between versions, requiring significant refactoring of existing codebases
Does not natively support some advanced inference methods or learning algorithms that are available in more mature libraries like TensorFlow Probability
Fit analysis
Who is it for?
✓ Best for
Developers building systems that require handling of uncertain data
Researchers working on machine learning projects involving Bayesian networks
Data analysts who need to model complex relationships in their datasets
✕ Not a fit for
Projects requiring real-time probabilistic inference with strict latency requirements
Applications where the underlying assumptions of graphical models do not hold
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with pgmpy
Step-by-step setup guide with code examples and common gotchas.