Unformatted text preview: all probability
distribution for the output variable, either in terms of its
probability density function (or mass function for discrete
output variables) or its cumulative distribution function.
(See sidebar on cost Scurves.) Some techniques for
this are:
The Cost SCurve
The cost Scurve gives the probability of a project's
cost not exceeding a given cost estimate. This
probability is sometimes called the budget
confidence level. This curve aids in establishing the
amount of contingency and Allowance for Program
Adjustment (APA) funds to set aside as a reserve
against risk. In the Scurve shown above, the project's cost
commitment provides only a 40 percent level of
confidence; with reserves, the level is increased to
50 percent. The steepness of the Scurve tells the
project manager how much the level of confidence
improves when a small amount of reserves are
added.
Note that an Estimate at Completion (EAC)
Scurve could be used in conjunction with the risk
management approach described for TPMs (see
Section 4.9.2), as another method of cost status
reporting and assessment meet. NASA Systems Engineering Handbook
Systems Analysis and Modeling Issues
•
•
• Analytic solution
Decision analysis
Monte Carlo simulation. Analytic Solution. When the structure of a model and
its uncertainties permit, a closedform analytic solution
for the required probability density (or cumulative
distribution) function is sometimes feasible. Examples
can be found in simple reliability models (see Figure
29).
Decision Analysis. This technique, which was
discussed in Section 4.6, also can produce a cumulative
distribution function, though it is necessary to descretize
any continuous input probability distributions. The more
probability intervals that are used, the greater the
accuracy of the results, but the larger the decision tree.
Furthermore, each uncertain model input adds more
than linear computational complexity to that tree,
making this technique less efficient in many situations
than Monte Carlo simulation, described next.
Monte Carlo Simulation. This technique is often used
to calculate an approximate solution to a stochastic
model that is too complicated to...
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 Spring '13
 Mr.Kau
 Systems Engineering, The American, ... ...

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