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Unformatted text preview: be solved by analytic
methods alone. A Monte Carlo simulation is a way of
sampling input points from their respective domains in
order to estimate the probability distribution of the output variable. In a simple Monte Carlo analysis, a value for
each uncertain input is drawn at random from its
probability distribution, which can be either discrete or
continuous. This set of random values, one for each
input, is used to compute the corresponding output
value, as shown in Figure 28. The entire process is then
repeated k times. These k output values constitute a
random sample from the probability distribution over the
output variable induced by the input probability
distributions.
For an example of the usefulness of this
technique, recall Figures 2 (in Chapter 2) and 24 (this
chapter), which show the projected cost and
effectiveness of three alternative design concepts as
probability "clouds." These clouds may be reasonably
interpreted as the result of three systemlevel Monte
Carlo simulations. The information displayed by the
clouds is far greater than that embodied in point
estimates for each of the alternatives.
An advantage of the Monte Carlo technique is
that standard statistical tests can be applied to estimate
the precision of the resulting probability distribution. This
permits a calculation of the number of runs (samples)
needed to obtain a given level of precision. If computing
time or costs are a significant constraint, there are
several ways of reducing them through more deliberate
sampling strategies. See MSFCHDBK1912, Systems
Engineering (Volume 2), for a discussion of these
strategies.
Commercial software to perform Monte Carlo
simulation is available. These include addin packages
for some of the popular spreadsheets, as well as
packages that allow the systems or program analyst to
build an entire Monte Carlo model from scratch on a
personal computer. These packages generally perform
the needed computations in an efficient manner and
provide graphical displays of the results, which is very
helpful in communicating probabilistic inform...
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This document was uploaded on 02/26/2014 for the course E 515 at University of Louisiana at Lafayette.
 Spring '13
 Mr.Kau
 Systems Engineering, The American, ... ...

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