Bayesian Classifier Implementation in Pattern Recognition

This article presents the application of Bayesian classifiers in pattern recognition and provides a comprehensive MATLAB implementation. The implementation includes key functions for parameter estimation using maximum likelihood approach and probability density calculation for different classes. We employ distinct color coding to visually distinguish correctly classified points (typically marked in blue) from misclassified points (usually highlighted in red), creating an intuitive visualization of classifier performance. Through this practical example, readers can gain deeper understanding of Bayesian classifier principles including prior probability calculation, likelihood estimation, and posterior probability computation. The MATLAB code demonstrates core algorithms such as Gaussian distribution parameter estimation and decision boundary formation, helping readers learn how to implement Bayesian classifiers efficiently in MATLAB. This implementation serves as an educational foundation for further exploration in pattern recognition domain.