Monte Carlo simulation

(noun)

a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results--i.e., by running simulations many times over in order to calculate those same probabilities

Related Terms

Examples of Monte Carlo simulation in the following topics:

  • Chance Models

    • Stochastic models help to assess the interactions between variables and are useful tools to numerically evaluate quantities, as they are usually implemented using Monte Carlo simulation techniques .
    • A stochastic model would be able to assess this latter quantity with simulations.
    • Stochastic models can be simulated to assess the percentiles of the aggregated distributions.
    • In a simulated stochastic model, the simulated losses can be made to "pass through" the layer and the resulting losses are assessed appropriately.
    • Monte Carlo simulation (10,000 points) of the distribution of the sample mean of a circular normal distribution for 3 measurements.

  • The Correction Factor

    • This property is often exploited in a wide variety of applications, including general problems of statistical estimation and machine learning, to estimate (probabilistic) quantities of interest via Monte Carlo methods.

  • Expected Value

    • This property is often exploited in a wide variety of applications, including general problems of statistical estimation and machine learning, to estimate (probabilistic) quantities of interest via Monte Carlo methods.