Bayesian Network Prediction Model with MATLAB Implementation

The MATLAB-based Bayesian network prediction model is a probabilistic and statistical forecasting framework capable of predicting event occurrence probabilities. This model employs directed acyclic graphs (DAGs) with conditional probability tables (CPTs) to represent variable dependencies. Key implementation features include the Bayesian Network Toolbox for structure learning (using PC, K2, or Hill-Climbing algorithms) and probability inference (utilizing junction tree or variable elimination methods). The model's applications span multiple domains: in finance, it supports stock price forecasting and risk assessment through historical market data integration; in healthcare, it enables disease prediction and diagnostic support by modeling symptom-disease relationships; in environmental science, it facilitates weather prediction and pollutant emission control via multivariate environmental parameter analysis. Mastering this MATLAB implementation provides significant advantages for data analysis professionals, offering flexible network customization through functions like learn_struct for structure learning and bn_inference for probability propagation, making it invaluable for decision-support systems across industries.