The paper summarizes some important results at the intersection of the fields of Bayesian statistics and stochastic simulation. Two statistical analysis issues for stochastic sim...
A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for efficient solution of statistical inverse problems governed b...
Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities b...
The tolerancing process links the virtual and the real worlds. From the former, tolerances define a variational geometrical language (geometric parameters). From the latter, there...
In any real-life identification problem, only a finite number of data points is available. On the other hand, almost all results in stochastic identification pertain to asymptotic...