Simulation of Minimum Error Rate Bayes Classifier Implementation

We developed a MATLAB simulation for a minimum error rate Bayes classifier, implementing specialized programs that handle both equal and unequal covariance matrix scenarios. The core algorithm involves calculating posterior probabilities using multivariate Gaussian density functions, where we implemented distinct discriminant functions for each class based on Bayesian decision theory. For the equal covariance case, we simplified the classification boundary to linear form using shared covariance matrix estimation, while the unequal covariance scenario required quadratic boundary calculations through class-specific covariance matrices. Our implementation includes data generation using multivariate normal distributions with specified parameters, followed by boundary plotting routines that visualize decision regions for three classes using contour plotting techniques. Additionally, we integrated performance evaluation metrics including accuracy, recall, and F1-score calculations to comprehensively assess classifier performance through confusion matrix analysis and statistical validation methods.