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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The availability of these tools, techniques and software packages have further promoted the application of Monte Carlo techniques within Australian design flood estimation practice.
The Monte Carlo technique has since been developed by scientists for use in radiotherapy treatments for cancer patients.
The [PD.sub.new] values were stored in specific files, as well as the values calculated by the Monte Carlo techniques, which were previously normalized with respect to the dose percent maximum located at the 3.25 cm depth below the water surface inside the phantom and over the radiation beam symmetry.
The ideas proposed for integration have inspired similar ideas for efficiency improvement when Monte Carlo techniques are used for simulation, and these prove to be especially valuable for VLSI circuit simulation where the dimensionality and complexity are very high.
They cover dosimetry and the radiation biology of the brain, adjunct Monte Carlo techniques, and a homogeneous thermal neutron fluence in a prescribed volume.
An effort to fully model the photon detection region with Monte Carlo techniques is in progress.
This work utilizes Monte Carlo techniques to couple physical range-energy data to measurements of chords (straight-line paths) of bone and marrow cavities.
Stochastic pricing, on the other hand, uses Monte Carlo techniques to repeatedly simulate insured lifetimes from which a distribution of policy outcomes is created.
Quasi Monte Carlo techniques, such as those in Li (2000) and Siegl and Tichy (2000), overcome the problems of the random number generator by using well-spaced grids of points, which are particularly effective at mapping the boundary of the problem even with a relatively small batch size.
Monte Carlo techniques were used to test the appropriateness of statistical procedures when the underlying assumptions of these procedures are violated.
Methods for improved calculation of these effects are under development by Monte Carlo techniques or direct solution of the drift kinetic equation.
Among specific topics are the decomposition of the optical polarization components of the Crab pulsar and its nebula, the analysis of single pulses from radio pulsars at high observing frequencies, the population synthesis of normal radio and gamma-ray pulsars using Markov chain Monte Carlo techniques, symmetry energy effects in the neutron star properties, and radio timing observations of four gamma-ray pulsars at Nanshan.