Monte Carlo Method Implementation Program

The Monte Carlo method is a numerical computation technique based on random sampling, whose fundamental concept involves performing multiple random simulations of the original problem to obtain approximate solutions. This implementation provides core source code supporting various distribution types for random variables. For instance, developers can directly utilize built-in implementations for normal distribution, uniform distribution, exponential distribution, and other common statistical distributions. The code architecture allows for precision enhancement through customizable parameters - users can modify random number seeds and increase simulation iterations to improve accuracy. Key functions include distribution parameter configuration, random number generation algorithms, and statistical analysis modules that collectively enable more precise numerical computation results. The object-oriented design facilitates easy extension for additional distribution types while maintaining computational efficiency through optimized sampling algorithms.