3901269
Description
SIMULATION INPUT MODELING
- Successful input
modeling
requires a close
match between
the input model
and the true
underlying
probabilistic
mechanism
associated with
the system.
- COLLECTING DATA
- The first is the classical
approach, where a
designed experiment is
conducted to collect the
data.
- The second is the
exploratory approach,
where questions are
addressed by means
of existing data that
the modeler had no
hand in collecting.
- there are several
things that can be
“wrong” about the
data set
- Wrong amount of aggregation.
- Wrong distribution in time.
- Wrong distribution in space.
- Censored data.
- Insufficient distribution resolution.
- Wrong amount of aggregation.
- The first is the classical
approach, where a
designed experiment is
conducted to collect the
data.
- Input models
- assessing independence.
- autocorrelation
- analysis of this data set includes
plotting a histogram and calculating
the values of some sample statistics
- to be determined is whether a parametric or nonparametric
model should be chosen for the process
- to be determined is whether a parametric or nonparametric
model should be chosen for the process
- analysis of this data set includes
plotting a histogram and calculating
the values of some sample statistics
- autocorrelation
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