Implementation of a Classifier for Samples Following Multivariate Normal Distribution

In pattern recognition, we need to implement a classifier capable of categorizing samples that follow multivariate normal distribution. This classifier learns the features and attributes of samples to determine their corresponding categories. Through this classifier, we can predict unknown samples and assign them to appropriate classes. During implementation, key considerations include statistical parameters such as sample mean vectors and covariance matrices, along with selecting appropriate classification algorithms. The implementation typically involves calculating the probability density function using multivariate normal distribution parameters, where the discriminant function for each class can be computed using the Mahalanobis distance. In code, this would require functions for parameter estimation (mean and covariance calculation) and classification logic based on maximum likelihood or Bayesian decision theory. This approach enhances the accuracy and reliability of pattern recognition systems by leveraging statistical properties of the data distribution.