MATLAB Implementation of Bayesian Classification Experiment

In this experiment, we will explore the application of Bayesian classifiers and design a simple linear classifier to understand fundamental pattern recognition methods. Our implementation in MATLAB will include probability density estimation using Gaussian distributions and decision boundary calculation based on Bayes' theorem. Specifically, we will code the classifier to: - Compute prior probabilities from training data - Estimate class-conditional probabilities using maximum likelihood estimation - Calculate posterior probabilities using Bayes' formula - Implement linear decision boundaries through discriminant function analysis By mastering the methodology of designing classifiers using Bayesian formulas with practical MATLAB implementation, we will gain deeper insights into the concepts and techniques in this field. The hands-on experiment will reinforce understanding through actual coding practice, including parameter tuning and performance evaluation using confusion matrices.