Documentation

Sponsor

Tip

You can support this project in various ways ❤️

• Star this repo at the github page.

• Become a Sponsor!

• Buy me a coffee! I ❤️ coffee :)

• Medium: medium profile and press follow.

• Substack: profile where you can subscribe.

• Gumroad: profile. where you can buy.

• You can also buy a digital educational product over here.

• Contribute in the form of feature requests, idea discussions, reporting bugs, opening pull requests.

Cheers Mate.

Medium Blog Posts

Note

• The Starters Guide to Causal Structure Learning with Bayesian Methods in Python.

• Why Prediction Isn’t Enough: Using Bayesian Models to Change the Outcome.

• Synthetic Data: The Essentials Of Data Generation Using Bayesian Sampling.

GitHub Repository

Note

Source code of bnlearn can be found at GitHub

Google Colab Notebooks

Note

• General Functionalities

• Inferences on the Salary Dataset

• Knowledge-Driven Approach

Citing bnlearn

The BibTeX citation can be found in the right side menu at the GitHub page.

References

• Probabilistic Graphical Models using pgmpy

• Causality, Pearl, 2009, 2nd Edition

• If Correlation Doesn’t Imply Causation, Then What Does? by Michael Nielsen

• Lecture Notes from Jonas Peters

• Elements of Causal Inference

• Causality Slides

Related Packages

• Causal Graphical Models

• Causality

• Causal Inference

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