Bayesian Network MATLAB Implementation

We present a comprehensive MATLAB implementation of Bayesian networks designed specifically for classification tasks. This toolbox provides robust probabilistic modeling capabilities using directed acyclic graphs (DAGs) to represent variables and their conditional dependencies. The implementation includes fundamental algorithms for Bayesian inference, parameter learning through maximum likelihood estimation or Bayesian approaches, and structure learning using score-based or constraint-based methods. Key functions handle probability propagation, evidence incorporation, and predictive classification with uncertainty quantification. The code supports discrete variables with conditional probability tables (CPTs) and includes optimization techniques for efficient computation. Suitable for machine learning applications, decision support systems, and medical diagnosis frameworks, this implementation allows users to construct, analyze, and validate Bayesian networks through intuitive MATLAB interfaces. Download links are provided below for testing and evaluation, and we welcome performance feedback and implementation suggestions.