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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On that account the Monte Carlo Method is skipped and the imported scatter plot is visualized.
As discussed previously, the difference between the Kolmogoroff-Avrami method and the Monte Carlo method occurs when the growth of crystals is anisotropic.
Attempts at the structure solution of 2-toluenesulfonamide (IV) using the Monte Carlo method were unsuccessful.
According to findings we can say that Monte Carlo method is a useful method for finding the best site plan layout for keeping dust down.
The main objectives of this paper were: firstly, our goal was to develop the evaluation procedures based on Monte Carlo Method for real and financial energetic contracts and secondly we tried to develop a new technique for the variation decreasing which could be applied to early contracts.
The furnished rooms with surfaces that have different optical properties, with the advantage of the stochlastic (probability) luminosity calculation, are often referred to as the Monte Carlo method.
In the past, clinicians had limited treatment choices or have been unable to include Monte Carlo method calculations into their routine for dose calculation due to numerous factors such as the extreme amount of time needed, restrictions of treatment planning software to a single machine or treatment modality and its integration into available software.
The topics include variance reduction techniques, positron emission tomography, the MCNP Monte Carlo program, the EGS family of code systems, the design of molecular imaging systems assisted by the Monte Carlo method, and applying the Monte Carlo method in nuclear medical dosimetry.
A new appendix reviews the incident of the Markov Chain Monte Carlo method and other adventures of the theory and its applications since the book's original publication in 1986 by North Holland Publishing Company, Amsterdam and Akademiai Kiado in Budapest.
Some paper topics include finite element simulation of oxygen out-diffusion in Ag-MgO composites, numerical prediction of the effective thermal conductivity of perforated hollow sphere structures, and lattice-based walks and the Monte Carlo method for addressing mass, thermal, and elasticity problems.
In the computational analysis, a statistical method of design variables separation (Wolny, 1992) and a procedure for the optimization of significant variables by the Monte Carlo method (Findeisen et al.

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