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.

You can perform Monte Carlo analysis with MATLAB and Simulink. MATLAB and Statistics Toolbox let you vary uncertain parameters for your model. In Simulink, you can create dynamic simulations and alter parameters with statistical uncertainty. With both MATLAB and Simulink you can:

• Create a Monte Carlo simulation to model a complex dynamic system
• Distribute simulations between processor cores and individual PCs to speed analysis
• Analyze data through robust plotting and advanced statistical methods

MATLAB Examples and How To

• Pricing American Basket Options by Monte Carlo Analysis (Example)
• Monte Carlo Analysis of a PK/PD Model for an Antibacterial Agent (Example)
• Introduction to Parallel Computing (Example)
• Monte Carlo Method for Computing Pi with PARFOR Loop (Example)
• Simulating Dependent Random Variables Using Copulas (Example)
• Developing and Implementing Scenario Analysis Models to Measure Operational Risk (User Story)

Simulink Examples and How To

• Shorten Parameter Sweeps with Parallel Computing 3:33 (Video)
• Robustness Analysis for Monte Carlo Simulations (Example)
• Improving System Models Using Monte Carlo Techniques (AIAA Paper)
• PID Tuning with Reference Tracking and Plant Uncertainty (Example)

Software Reference

• Monte Carlo Simulation of Conditional Variance Models (Documentation)
• Uncertain State Space for Robust Control Design and Analysis (Function)
• Simulate Linear Model Uncertainty Using Monte Carlo Method (Function)
• Monte Carlo Analysis of Correlated Asset Returns (Function)

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