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, Predictive Modeling

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