Monte Carlo Simulation

Divides number of samples with system failure by total number of random samples generated to estimate probability of failure in reliability
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Updated 12 Jan 2024

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Monte Carlo simulation divides the number of samples with system failure by the total number of random samples generated to estimate the probability of failure in reliability analysis. Monte Carlo simulations help to explain the impact of risk and uncertainty in prediction and forecasting models. Monte Carlo simulation involves three steps:
  • Randomly generate “N” inputs (N is the number of experiment).
  • Run a simulation for each of the “N” inputs. Simulations are run on a computerized model of the system being analyzed.
  • Common measures include the mean value of an output, the distribution of output values, and the minimum or maximum output value.
A larger number of experiments lead to more accurate and stable estimates reduces the effect of randomness and provides a better understanding of the system.
Increasing the number of experiment the availability of the system also increase.
Mean Time To Failure (MTTF) is the average time a non-repairable part or piece of equipment remains in operation until it needs to be replaced. If we increasing the mean time to failure in a Monte Carlo Simulation, it implies that we are extending the average time, a system or component operates before failing.

Cite As

Muhammad Ameer Hamza (2024). Monte Carlo Simulation (https://www.mathworks.com/matlabcentral/fileexchange/157501-monte-carlo-simulation), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0.0