In stochastic simulation, input modeling refers to the process of identifying and selecting the probability distributions, called input models, from which are generated the random variates that are the source of the stochastic variation in the simulation model when it is run. This article reviews the history of the development and use of such models with the main focus on discrete-event simulation (DES).</p
The statistics profession has been remiss in exploiting the numerous advances in simulation methodol...
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essential...
This chapter is a review of some simulation models, with special reference to social sciences. Three...
Input modeling is the selection of a probability distribution to capture the uncertainty in the inpu...
Input data modeling is a critical component of a successful simulation application. A perspective of...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
Stochastic simulation is an invaluable tool for operations-research practitioners for the performanc...
discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
An important, but often neglected, part of any sound simulation study is that of modeling each sourc...
The tutorial will be used to introduce some basic techniques for analysing the output of stochastic ...
Techniques are presented for modeling and randomly sampling many of the multivariate probabilistic i...
The steps of the process for conducting a simulation modeling and analysis project include: problem ...
The complete validation of an econometric model is a process which involves a formidable number of a...
We formulate and evaluate a Bayesian approach to probabilistic input modeling. Taking into account t...
The statistics profession has been remiss in exploiting the numerous advances in simulation methodol...
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essential...
This chapter is a review of some simulation models, with special reference to social sciences. Three...
Input modeling is the selection of a probability distribution to capture the uncertainty in the inpu...
Input data modeling is a critical component of a successful simulation application. A perspective of...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
Stochastic simulation is an invaluable tool for operations-research practitioners for the performanc...
discrete-event simulation models have stochastic elements that mimic the probabilistic nature of the...
Most discrete-event simulation models have stochastic elements that mimic the probabilistic nature o...
An important, but often neglected, part of any sound simulation study is that of modeling each sourc...
The tutorial will be used to introduce some basic techniques for analysing the output of stochastic ...
Techniques are presented for modeling and randomly sampling many of the multivariate probabilistic i...
The steps of the process for conducting a simulation modeling and analysis project include: problem ...
The complete validation of an econometric model is a process which involves a formidable number of a...
We formulate and evaluate a Bayesian approach to probabilistic input modeling. Taking into account t...
The statistics profession has been remiss in exploiting the numerous advances in simulation methodol...
Simulation Modeling and Analysis with Arena is a highly readable textbook which treats the essential...
This chapter is a review of some simulation models, with special reference to social sciences. Three...