Bayesian analysis for simulation input and output
Proceedings of the 29th conference on Winter simulation
Problems in Bayesian analysis of stochastic simulation
WSC '86 Proceedings of the 18th conference on Winter simulation
Regression metamodeling in simulation using Bayesian methods
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Reducing input parameter uncertainty for simulations
Proceedings of the 33nd conference on Winter simulation
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Corrigendum: New Selection Procedures
Operations Research
Simulation input modeling: a kernel approach to estimating the density of a conditional expectation
Proceedings of the 35th conference on Winter simulation: driving innovation
Reducing parameter uncertainty for stochastic systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Kriging interpolation in simulation: a survey
WSC '04 Proceedings of the 36th conference on Winter simulation
A Bayesian approach to analysis of limit standards
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
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Winter Simulation Conference
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
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Bayesian methods are useful in the simulation context for several reasons. They provide a convenient and useful way to represent uncertainty about alternatives (like manufacturing system designs, service operations, or other simulation applications) in a way that quantifies uncertainty about the performance of systems, or about inputs parameters of those systems. They also can be used to improve the efficiency of discrete optimization with simulation and response surface methods. Bayesian methods work well with other decision theoretic tools, and can therefore provide a link from traditional operations-level experiments to higher-level managerial decision-making needs, in addition to improving the efficiency of computer experiment