Monte Carlo simulation

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Monte Carlo simulation

An analytical technique for solving a problem by performing a large number of trail runs, called simulations, and inferring a solution from the collective results of the trial runs. Method for calculating the probability distribution of possible outcomes.

Monte Carlo Simulation

A computer simulation that seeks to determine the likelihood of various scenarios by running multiple simulations using random variables. The results of the Monte Carlo simulation show the most likely outcomes.

Monte Carlo simulation.

A Monte Carlo simulation can be used to analyze the return that an investment portfolio is capable of producing. It generates thousands of probable investment performance outcomes, called scenarios, that might occur in the future.

The simulation incorporates economic data such as a range of potential interest rates, inflation rates, tax rates, and so on. The data is combined in random order to account for the uncertainty and performance variation that's always present in financial markets.

Financial analysts may employ Monte Carlo simulations to project the probability of your retirement account investments producing the return you need to meet your long-term goals.

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To help them do that, financial advisors use Monte Carlo Simulations, which can show the probability of investment success under different scenarios before any investments are made.
Monte Carlo simulation (MCS) is a widely used technique in the risk analysis of the energy projects.
It's time to roll the dice using a Monte Carlo simulation.
To test the usefulness of interval analysis as a method for error analysis, an error analysis was conducted with both interval analysis and Monte Carlo simulations on the layer-shift method used to characterize a commercial MagRAM (magnetic radar absorbing material) at 10 GHz with nominal [[epsilon].
The Monte Carlo simulation has been developed as an improved scenario analysis.
Monte Carlo simulation is so named because it refers to the games of chance played in the casinos of Monte Carlo.
It is this inherent randomness that give a Monte Carlo simulation its name, since the random numbers it incorporates are not that dissimilar from the various games of chance found in a casino.
21) Excellent agreement between measurement and simulation on the graph shows successful dose prediction by Monte Carlo simulations.
By using Monte Carlo simulations, it is possible to show a client the relation-ship between risk and return, says John D.
Useful websites for learning more about Monte Carlo simulations include www.
Finite-element methods along with Monte Carlo simulations were used to design a magnetic storage device for ultracold neutrons (UCN) to measure their lifetime.
Optical coefficients were derived using an inverse adding doubling program, and Monte Carlo simulations were carried out to evaluate light transport in the tissue phantoms.

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