Analysis Methodology
Track Coordinators: Bruno Tuffin, INRIA & Seong-Hee Kim, Georgia Tech
Simulation analysis covers a variety of mathematical, statistical, empirical, and computational methods. The Analysis Methodology track includes papers on input, output and model analysis. Input analysis tries to improve the quality of the inputs (random variates, distributions, etc.) to a simulation. Output analysis aims to meaningfully interpret simulation outputs to draw informative inferences regarding the underlying simulation model. Model analysis deals with the efficiency and appropriateness of a simulation in providing useful estimates. The main focus of this track is on how to obtain better input, estimates or inference by using efficient approaches or algorithms. We also welcome suggestions for sessions on emerging topics. Nonconventional methods are of particular interest.
Topics of interest include, but are not limited to, the following:
- Sensitivity and optimization methods
- Ranking and selection
- Input process modeling
- Random variate generation
- Variance reduction
- Rare-event simulation
- Simulation of financial processes
- Markov chain Monte Carlo methods
- Analytic representations of simulation models
- Metamodeling
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