This project implements various sampling techniques for Bayesian Networks, including Prior Sampling, Rejection Sampling, Likelihood Weighting, and Gibbs Sampling. It also includes comparison experiments between these techniques.
main.py: The main script for running the experiments.weather.json: Example data file representing a Bayesian network.error_vs_samples.png: Output plot generated from the experiments.
python >= 3.9
Navigate to the project directory and run the following command:
python ./main.pyThe script will print the results of the sampling experiments in the terminal. Additionally, figures showing the comparison of errors vs. sample sizes will appear in a separate window.
Output figures will be automatically saved in the current directory after the script finishes running.
The program outputs:
- The estimated probabilities for the query node using different sampling techniques.
- A plot comparing the mean error vs. the number of samples for Rejection Sampling, Likelihood Weighting, and Gibbs Sampling.
The plot is saved as error_vs_samples.png in the project folder and is displayed after the experiments are run. Multiple figures might be generated, and you'll need to close each one before the next appears.
