A lightweight Python module for Bayesian Missing Data Imputation using probabilistic modeling and MCMC sampling.
This tool estimates missing values in numeric tabular datasets by modeling each variable as a distribution and inferring the posterior over missing entries using PyMC.
- Bayesian imputation using normal priors and MCMC sampling.
- Handles missing values in numeric (continuous) columns.
- Provides posterior summaries and visual comparison plots.
- Designed for easy extension and experimentation.
Clone this repo and install dependencies:
pip install -r requirements.txt