# 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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Using the rubric interpreted from previous SQLS-R4 studies that a sample size of N=100 is adequate to produce an acceptable fit to data, the current study sought to determine the minimum sample size required to offer a good fit to data within an adequately powered study using Monte Carlo simulation (22).
Steps involved in the event-based joint probability approach, implemented via Monte Carlo simulation, are as follows:
The Monte Carlo simulation technique, named for the famous Monaco gambling resort, originated during World War II as a way to model potential outcomes from a random chain of events.
One of the most serious problems of Monte Carlo simulation for grain growth is that the correspondence between Monte Carlo steps and real time is not well understood.
The proposed combination method is checked by the Monte Carlo simulations. The usefulness of the presented approach is demonstrated by a numerical example of a ropeway gondola induced by wind and by a design example of the dynamic vibration absorber.
Monte Carlo simulation (MCS) is a widely used technique in the risk analysis of the energy projects.
SEAMCAT Monte Carlo simulation results show the required separation distance between the LTE BS (downlink) and ARNS ground receiver (only RSBN was considered).
The mean and the 95% probability interval (PI) of the mean, median, and 25th and 75th percentile serum concentrations at birth (ng/mL), across 10,149 participants for each of the 12 Monte Carlo simulations (500 iterations per simulation).
Monte Carlo Simulation 1 2 3 4 13 5 35 6 26 7 19 8 7 9 Finally, collect and analyze the findings.
When new risks arise, they are added to the risk register report and their impacts are run through the Monte Carlo simulation. The resulting reports should be reviewed regularly and shared with the project supervisor.
Saager and Berger [9] utilized a dual-detector system and least squares method to remove top-layer-only fluctuations and validated the effect of the methodology by performing Monte Carlo simulations based on a two-layer turbid media model.

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