References
- Barton RR (1998). Simulation metamodels. Proceedings of the 1998 Winter Simulation Conference. IEEE: Washington DC, pp 167–174.
- Barton RR, Nelson BL and Xie W (2010). A framework for input uncertainty analysis. Proceedings of the 2010 Winter Simulation Conference. IEEE: Baltimore, MD, pp 1189–1198.
- Batarseh OG and Wang Y (2008). Reliable simulation with input uncertainties using an interval-based approach. Proceedings of the 2008 Winter Simulation Conference. IEEE: Miami, FL, pp 344–352.
- BillerBCopula-based multivariate input models for stochastic simulationOperations Research200957387889210.1287/opre.1080.0669
- BillerBCorluCGAccounting for parameter uncertainty in large-scale stochastic simulations with correlated inputsOperations Research201159366167310.1287/opre.1110.0915
- Biller B and Gomez C (2010). Capturing parameter uncertainty in simulations with correlated inputs. Proceedings of the 2010 Winter Simulation Conference. IEEE: Baltimore, MD, pp 1167–1177.
- BuntineWA guide to the literature on learning probabilistic networks from dataIEEE Transactions on Knowledge and Data Engineering19968219521010.1109/69.494161
- Charnes JM (1995). Analyzing multivariate output. Proceedings of the 1995 Winter Simulation Conference. IEEE: Arlington, VA, pp 201–208.
- ChengRCHHollandWSensitivity of computer simulation experiments to errors in input dataJournal of Statistical Computation and Simulation199757121924110.1080/00949659708811809
- ChengRCHHollandWTwo-point methods for assessing variability in simulation outputJournal of Statistical Computation and Simulation199860118320510.1080/00949659808811887
- ChengRCHHollandWCalculation of confidence intervals for simulation outputACM Transactions on Modeling and Computer Simulation200414434436210.1145/1029174.1029176
- Chick SE (1997). Bayesian analysis for simulation input and output. Proceedings of the 2008 Winter Simulation Conference. IEEE: Atlanta, GA, pp 253–260.
- ChickSESelection for simulation experiments: Accounting for input uncertaintyOperations Research200149574475810.1287/opre.49.5.744.10606
- Dash D and Druzdzel MJ (1999). A hybrid anytime algorithm for the construction of causal models from sparse data. Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann: Stockholm, Sweden, pp 142–149.
- DavidsonRMacKinnonJGEstimation and Inference in Econometrics1993
- DeanTKanazawaKA model for reasoning about persistence and causationComputational Intelligence199053142150
- Decision Systems Laboratory (2010a). GeNIe (graphical network interface), http://genie.sis.pitt.edu/, accessed 16 March.
- Decision Systems Laboratory (2010b). SMILE (structural modeling, inference, and learning engine). Available via http://genie.sis.pitt.edu/, accessed 16 March.
- Dougherty J, Kohavi R and Sahami M (1995). Supervised and unsupervised discretization of continuous features. Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann: Tahoe City, CA, pp 194–202.
- FriedmanLWSystems simulation. Design and analysis of multivariate response simulations: The state of the artBehavioral Science198732213814810.1002/bs.3830320206
- FriedmanLWThe Simulation Metamodel1996
- Friedman N and Goldszmidt M (1996). Discretizing continuous attributes while learning Bayesian networks. Proceedings of the 13th International Conference on Machine Learning. Morgan Kaufmann: Bari, Italy, pp 157–165.
- HeckermanDBayesian networks for data miningData Mining and Knowledge Discovery1997117911910.1023/A:1009730122752
- HeckermanDLearning in Graphical Models1999301354
- Henderson SG (2003). Input model uncertainty: Why do we care and what should we do about it? Proceedings of the 2003 Winter Simulation Conference. IEEE: New Orleans, LA, pp 90–100.
- HowardRAMathesonJEInfluence diagramsDecision Analysis20052312714310.1287/deca.1050.0020
- JensenFVBayesian Networks and Decision Graphs (Information Science and Statistics)2001
- JensenFVNielsenTDBayesian Networks and Decision Graphs2007
- KilmerRASmithAEShumanLAComputing confidence intervals for stochastic simulation using neural network metamodelsComputers & Industrial Engineering199936239140710.1016/S0360-8352(99)00139-4
- KleijnenJPCDesign and Analysis of Simulation Experiments2008
- KleijnenJPCSimulation experiments in practice: Statistical design and regression analysisJournal of Simulation200821192710.1057/palgrave.jos.4250032
- LawASimulation Modeling and Analysis2006
- MattilaVAVirtanenKRaivioTImproving maintenance decision making in the Finnish air force through simulationInterfaces200838318720110.1287/inte.1080.0349
- MerrickJRWvan DorpJRDineshVAssessing uncertainty in simulation-based maritime risk assessmentRisk Analysis200525373174310.1111/j.1539-6924.2005.00616.x
- MiltonJSArnoldJCProbability and Statistics in the Engineering and Computing Sciences1986
- MyersRHResponse surface methodology: A retrospective and literature surveyJournal of Quality Technology20043615377
- NeapolitanRELearning Bayesian Networks2004
- NelsenRBAn introduction to Copulas2006
- NgSHChickSEReducing parameter uncertainty for stochastic systemsACM Transactions in Modeling and Computer Simulation2006161265110.1145/1122012.1122014
- PearlJProbabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference1991
- PhillipsGMInterpolation and Approximation by Polynomials2003
- Poropudas J (2011). Bayesian networks, influence diagrams, and games in simulation metamodeling. Doctoral dissertation, Aalto University School of Science, http://lib.tkk.fi/Diss/2011/isbn9789526042688/, accessed 16 March 2012.
- Poropudas J and Virtanen K (2007). Analyzing air combat simulation results with dynamic Bayesian networks. Proceedings of the 2007 Winter Simulation Conference. IEEE: Washington DC, pp 1370–1377.
- Poropudas J and Virtanen K (2009). Influence diagrams in analysis of discrete event simulation data. Proceedings of the 2009 Winter Simulation Conference. IEEE: Austin, TX, pp 696–708.
- Poropudas J and Virtanen K (2010). Simulation metamodeling in continuous time using dynamic Bayesian networks. Proceedings of the 2010 Winter Simulation Conference. IEEE: Baltimore, MD, pp 935–946.
- PoropudasJVirtanenKSimulation metamodeling with dynamic Bayesian networksEuropean Journal on Operational Research2011214364465510.1016/j.ejor.2011.05.007
- PortaNde OAMWilsonJREstimation of multiresponse simulation metamodels using control variatesManagement Science198935111316133310.1287/mnsc.35.11.1316
- ScholzFWStephensMAK-sample Anderson–Darling testsJournal of the American Statistical Association198782399918924
- SpirtesPGlymourCScheinesRCausation, Prediction, and Search (Adaptive Computation and Machine Learning)2001
- ZobelCWKeelingKBNeural network-based simulation metamodels for predicting probability distributionsComputers and Industrial Engineering200854487988810.1016/j.cie.2007.08.012
- ZouaouiFWilsonJRAccounting for uncertainty in simulation input modelingIIE Transactions200335378179210.1080/07408170304413
- ZouaouiFWilsonJRAccounting for input-model and input-parameter uncertainties in simulationIIE Transactions200436111135115110.1080/07408170490500708