Monte Carlo Simulation

Perform sensitivity analysis through random parameter variation

Monte Carlo simulation is a method for exploring the sensitivity of a complex system by varying parameters within statistical constraints. These systems can include financial, physical, and mathematical models that are simulated in a loop, with statistical uncertainty between simulations. The results from the simulation are analyzed to determine the characteristics of the system.

Common tasks for performing Monte Carlo analysis include:

  • Varying uncertain parameters for your model
  • Creating dynamic simulations and alter parameters with statistical uncertainty
  • Creating a Monte Carlo simulation to model a complex dynamic system
  • Distributing simulations between processor cores and individual PCs to speed analysis
  • Analyzing data through robust plotting and advanced statistical methods

For details, see MATLAB®, Simulink® and Statistics and Machine Learning Toolbox™.

MATLAB Examples and How To

Simulink Examples and How To

Software Reference

See also: formal verification, financial engineering, random number, system verification and validation, Monte Carlo simulation in computational finance, parameter estimation, load forecasting, modeling and simulation, simulation software, Monte Carlo simulation videos