Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The simulation metamodel
Uniform and bootstrap resampling of empirical distributions
WSC '93 Proceedings of the 25th conference on Winter simulation
Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Proceedings of the 30th conference on Winter simulation
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Calculation of confidence intervals for simulation output
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
Learning Bayesian Networks
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Analyzing air combat simulation results with dynamic Bayesian networks
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Reliable simulation with input uncertainties using an interval-based approach
Proceedings of the 40th Conference on Winter Simulation
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Stochastic Kriging for Simulation Metamodeling
Operations Research
Game-theoretic validation and analysis of air combat simulation models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Influence diagrams in analysis of discrete event simulation data
Winter Simulation Conference
Simulation metamodeling in continuous time using dynamic Bayesian networks
Proceedings of the Winter Simulation Conference
Capturing parameter uncertainty in simulations with correlated inputs
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
Game theoretic simulation metamodeling using stochastic kriging
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
This paper proposes a novel approach to multiple input and multiple output (MIMO) simulation metamodeling using Bayesian networks (BNs). A BN is a probabilistic model that represents the joint probability distribution of a set of random variables and enables the efficient calculation of their marginal and conditional distributions. A BN metamodel gives a non-parametric description for the joint probability distribution of random variables representing simulation inputs and outputs by combining MIMO data provided by stochastic simulation with available background knowledge about the system under consideration. The BN metamodel allows various what-if analyses that are used for studying the marginal probability distributions of the outputs, the input uncertainty, the dependence between the inputs and the outputs, and the dependence between the outputs as well as for inverse reasoning. The construction and utilization of BN metamodels in simulation studies are illustrated with an example involving a queueing model.