MATLAB Implementation of Monte Carlo Simulation Algorithm

The Monte Carlo method is a stochastic simulation technique based on probability and statistical theory. It typically uses random numbers or pseudo-random numbers to solve various computational problems. The core concept involves associating the target problem with a specific probability model and then employing computer statistical simulation or sampling to obtain approximate solutions. In MATLAB implementations, this often involves generating random samples using built-in functions like rand (for uniform distribution) and randn (for normal distribution), defining appropriate probability models, and performing iterative simulations to converge toward accurate results. This method finds widespread applications across multiple fields including physics, finance, engineering, and computer science. Due to its stochastic nature and high flexibility, Monte Carlo simulation serves as a powerful tool for solving diverse practical problems. Algorithm implementation typically requires careful consideration of sample size, convergence criteria, and variance reduction techniques to optimize computational efficiency.