MATLAB Code Implementation for Random Number Generation

The M-file for random number generation in MATLAB contains numerous practical examples. These examples demonstrate fundamental approaches for implementing random number generators using core MATLAB functions like rand(), randn(), and randi(). The implementations cover various probability distributions including uniform, normal, and discrete distributions with configurable parameters such as mean, variance, and range limits. By studying these examples, developers can understand how to initialize random number streams using rng(), control random sequences for reproducible results, and apply distribution-specific functions like normrnd() and unifrnd(). The examples also showcase advanced techniques including multidimensional array generation and custom distribution implementations. Developers can extend these templates by incorporating additional statistical functions, modifying sampling algorithms, or integrating with MATLAB's Statistics and Machine Learning Toolbox for more sophisticated random variate generation tailored to specific application requirements.