Monte Carlo Simulation


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The analysis of a system’s behaviour, by randomly changing the system’s input, and observing output
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Bloschl and Sivapalan (1997) derived a more practically applicable Monte Carlo simulation technique.
Furthermore, all the Monte Carlo simulation and the mathematical, analytical results are agreed entirely with each other.
The Monte Carlo simulation was operated with 10000 simulation replications and the distributions of key variables were generated.
However, if a moderate-to-strong correlation or causality between the variables is believed to be present, Monte Carlo simulations are readily adapted using additional advanced techniques to incorporate this co-variation.
The concrete intervals of the Monte Carlo simulation for 1,000 draws, in keeping with hypothesis I3 and the three distinct scenarios, are detailed in Tab.
Comparing with the Monte Carlo simulation, the partial linearization procedure presented in Figure 3 greatly decreases the computational time.
In this paper, for performing Monte Carlo simulation, we have used an investment project from the energy sector, as a case study.
The lattice sites spacing [DELTA] cannot be ignored in the Monte Carlo simulation. A new equation that gives the real time as function of simulated time [t.sub.MCS], the temperature T, and the lattice sites spacing [DELTA] has been derived.
For each of the six PWDs, we used the following equation to generate multiple simulated drinking water concentrations (n = 500, using Monte Carlo simulation) by multiplying the originally estimated average PFOA drinking-water concentrations (Shin et al.
So far, we have used the Delphi method to consider perspectives and the dice (Monte Carlo simulation) to consider uncertainty.

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