MATLAB Implementation of Bayesian Networks

Below is a well-structured MATLAB implementation for Bayesian networks that enables training, inference, and prediction tasks. Bayesian networks are probabilistic graphical models used to represent probability relationships among multiple variables, widely applied in artificial intelligence, machine learning, and data analysis domains. This implementation includes key functions for parameter learning using maximum likelihood estimation or Bayesian estimation methods, and implements inference algorithms such as variable elimination or junction tree algorithm for probabilistic reasoning. The code structure separates network initialization, conditional probability table (CPT) learning, and evidence propagation modules, allowing users to modify network structures through adjacency matrix configuration. Through this implementation, users can gain deeper understanding of Bayesian network mechanisms and practical applications, with potential extensions supporting dynamic Bayesian networks and hybrid discrete-continuous variable handling.