Monte Carlo method

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Monte Carlo method

a computer trial or study which uses random numbers in the simulation and analysis of complex relationships and events.
References in periodicals archive ?
COMPUTER SIMULATIONS OF MARKOV CHAINS AND MONTE CARLO METHODS
Given the experience of the first financial period of the EU funds to Romania (2007-2013), as a country which has concluded an accession process, followed by one of having access to the European funds, and also from a start with a gap relatively similar in the second budget period (2014-2020), we can make assumptions and scenarios as to some developments, either stable or unstable, optimistic or pessimistic, by making use of the Monte Carlo method, and thus shaping a complex simulation of the level of EU funding that can be accessed by the national economy in the future.
When the probability is relatively small, the number of samples required by the Monte Carlo method in order to adequately make predictions increases significantly, making the analysis difficult.
More broadly, Monte Carlo methods are useful for modeling problems with significant uncertainty in inputs, such as optimization.
Because Monte Carlo methods use repetitive algorithms and a large hum her of calculations, they are best suited for computerized simulations.
ABLCC is completed by employing Monte Carlo methods, which are used to model liability and contingent costs and "costs that arise due to noncompliance and potential future liabilities.
In particular, he believes that Monte Carlo methods can be used to illuminate the idea of a sampling distribution, a fundamental concept that is often difficult for students to grasp.
Like many rendering programs, Radiance uses Monte Carlo methods to evaluate the integral on the right hand side of the radiance equation.
As an example, the authors critically review a 1994 article by Grinberg-Zylberbaum, Delaflor, Attie, and Goswami claiming significant correlation between the EEGs of isolated participants; however, using uncorrelated EEG data from one of our previous studies and Monte Carlo methods to model the true null hypothesis, the authors compute a nonsignificant difference (z=1.
XVII) Applications of dynamic Monte Carlo methods to polymeric hydrocarbons.
The differences between dynamic programming, Monte Carlo methods, and temporal difference learning are teased apart, then tied back together in a unified way.
The uncertainty about how subtle, hidden patterns among digits spewed out by various random-number generators may influence simulation results presents researchers using so-called Monte Carlo methods with a serious dilemma, especially when the answer is not known.
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