MATLAB Implementation of Bayesian Networks with Comprehensive Code Features

This package provides a comprehensive MATLAB-based implementation of Bayesian Networks, featuring core functionalities for network construction, model training, and statistical inference. The implementation includes key code modules for parameter estimation using Expectation-Maximization algorithms and probabilistic reasoning through junction tree or variable elimination methods. Additionally, it offers advanced utilities for model evaluation via likelihood scoring, model comparison using Bayesian information criterion (BIC), and automated model selection techniques. The latest Version 1.0.4 introduces significant computational enhancements, including Gaussian mixture models for continuous variable support with optimized kernel density estimation, improving both model accuracy and numerical stability. The update incorporates sparse matrix operations and memory-efficient data structures for handling large-scale datasets, enabling practical applications with high-dimensional data. Several bug fixes address edge cases in inference algorithms and improve user interface consistency. This robust Bayesian network implementation provides a versatile toolkit with continuously updated features, suitable for diverse applications ranging from medical diagnosis to financial risk assessment. The object-oriented design allows flexible extension of network structures and custom inference engines, making it adaptable to various research and industrial requirements.