MATLAB Code for Generating Random Processes

This repository contains a series of MATLAB source codes specifically designed for generating random processes. The collection includes implementations for generating Gaussian distributed random variables using functions like randn(), creating multivariate Gaussian process samples through covariance matrix decomposition techniques, and simulating Gauss-Markov processes using state-space models with appropriate transition matrices. All programs have been thoroughly tested through individual executions to ensure their correctness and reliability. Additionally, comprehensive comments have been incorporated within the code to facilitate better understanding of the underlying algorithms, such as the Box-Muller transform for Gaussian generation or Cholesky decomposition for multivariate cases. These annotations aim to help users not only utilize the code effectively but also adapt and apply these techniques to their own projects. We hope this resource proves valuable for researchers and practitioners, providing both practical solutions and inspiration for further developments in stochastic process simulation.