Selection of input models using bootstrap goodness-of-fit
WSC '96 Proceedings of the 28th conference on Winter simulation
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Bayesian model selection when the number of components is unknown
Proceedings of the 30th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Proceedings of the 33nd conference on Winter simulation
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Classification analysis for simulation of machine breakdowns
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
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This paper discusses the problem of fitting mixture models to input data. When an input stream is an amalgam of data from different sources then such mixture models must be used if the true nature of the data is to be properly represented. A key problem is then to identify the different components of such a mixture, and in particular to determine how many components there are. This is known to be a non-regular/non-standard problem in the statistical sense and is technically notoriously difficult to handle properly using classical inferential methods. We discuss a Bayesian approach and show that there is a theoretical basis why this approach might overcome the problem. We describe the Bayesian approach explicitly and give examples showing its application.