Simulation output clearly depends on the form of the input distributions used to drive the
model. Often these input distributions are fitted using finite samples of real-world data. The …

Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

Simulation models support managers in the solution of complex problems. International
agencies recommend uncertainty and global sensitivity methods as best practice in the …

We consider the problem of deriving confidence intervals for the mean response of a system
that is represented by a stochastic simulation whose parametric input models have been …

When we use simulation to estimate the performance of a stochastic system, the simulation
often contains input models that were estimated from real-world data; therefore, there is both …

We formulate and evaluate a Bayesian approach to probabilistic input modeling for
simulation experiments that accounts for the parameter and stochastic uncertainties inherent …

Input uncertainty refers to errors caused by a lack of complete knowledge about the
probability distributions used to generate input variates in stochastic simulation. The …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

Subjective probability and Bayesian methods provide a unified approach to handle not only
randomness from stochastic sample-paths, but also uncertainty about input parameters and …

Several major risk studies have been performed in recent years in the maritime
transportation domain. These studies have had significant impact on management practices …