pgmpy

What it is

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.

Pgmpy stands out as a comprehensive Python library for working with Probabilistic Graphical Models, offering both flexibility and depth in model creation and inference.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited scalability for large datasets

   Performance degrades significantly with an increase in the number of nodes and edges, making it impractical for real-time or big data applications

2. 02

   Poor documentation and examples

   The official documentation lacks comprehensive tutorials and practical examples, leading to a steep learning curve for new users

3. 03

   Active development but unstable API

   Frequent changes in the API can lead to breaking changes between versions, requiring significant refactoring of existing codebases

4. 04

   Limited support for advanced features and algorithms

   Does not natively support some advanced inference methods or learning algorithms that are available in more mature libraries like TensorFlow Probability

What it does well

• Support for Bayesian Networks and Markov Models

• Inference algorithms like Variable Elimination, Belief Propagation

• Parameter learning using Maximum Likelihood Estimation (MLE)

• Structure learning from data

• Visualization of models

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.