Monte Carlo Simulation
Monte Carlo simulation is a computational technique that uses random sampling and statistical modeling to estimate the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. It is widely used in finance, engineering, project management, and science to model risk and uncertainty in complex systems. By running thousands or millions of simulations with varying inputs, it provides a distribution of possible outcomes rather than a single deterministic result.
Developers should learn Monte Carlo simulation when building applications that involve risk analysis, financial modeling, or optimization under uncertainty, such as in algorithmic trading, insurance pricing, or supply chain management. It is particularly useful for problems where analytical solutions are intractable, allowing for scenario testing and decision-making based on probabilistic forecasts. In fields like machine learning and data science, it can be applied for hyperparameter tuning or Bayesian inference.