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Inference on Bayesian Networks

Project for the "Computer Science with Laboratory" course exam, UniPi, 2024/2025

This project allows to compute marginal and conditional probabilities given a discrete Bayesian Network (specified in a BIF file where CPT lines are written in inverse lexicographical order, see data/). To do so, it implements the Variable Elimination algorithm. I also made a simple Python code with the library pgmpy that computes the same thing to double-check the results.

To compile and run (using clang++ or g++ on macOS):

make run

To remove the directories obj/ and bin/:

make clean

To use the Python code:

cd python_version
python3.11 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Data

Files in the data/ folder are taken from the Bayesian Network Repository by Marco Scrutari.

License

This project is licensed under the MIT License — see the LICENSE file for details.

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