Monte Carlo Simulation is a
statistical method used to generate random variables for modeling uncertainty
and risk of a particular system. The random inputs or variables are modeled
through various probability distributions like lognormal, normal, uniform,
triangular, PERT, discrete, etc. Monte Carlo simulation has a number of
benefits over the standard single-point or deterministic analysis in that:
ü The
results provided show both what could happen and the likelihood of it to happen
ü Since the
data generated by Monte Carlo is detailed enough, it is easy for statisticians to
draw graphs to represent different outcomes as well as their likelihood of
occurrence. Having this information is important because it helps in
communicating findings to other stakeholders.
ü It is
difficult to see the variables that affect the outcome the most when using
deterministic analysis. When using Monte Carlo simulation, it is easy to find
out what variables have the biggest impact on the results.
ü When using
Monte Carlo method it is also possible to model relations between input
variables
Monte Carlo simulation has
enabled researches to perform multiple trials and determine potential outcomes
of an investment or event. It creates a probability, risk assessment, and
distribution for a given event or investment.
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