Simulation Input Mixture Models

Professor Russell Cheng

Multimodal data occurs frequently in discrete-event simulation input analysis occurring when an input sample stream arises from different sources. A finite mixture distribution is a simple input model for representing such data. However fitting this model to such data is not straightforward, with the problem well-known to be non-standard. Even though much studied, the two most common approaches, maximum likelihood (ML) and Bayesian reversible jump Markov chain Monte Carlo (RJMCMC), do not seem very satisfactory in our simulation application. We review the problems that arise with each approach, and then describe an alternative Bayesian approach, MAPIS, which uses maximum a posteriori estimation with importance sampling, showing it overcomes the main problems encountered with ML and RJMCMC. We demonstrate use of a publicly-available implementation of MAPIS, which we have called FineMix, applying it to practical examples from finance and manufacturing, comparing the results with those of ML and RJMCMC.

RUSSELL CHENG is Emeritus Professor at the University of Southampton, Mathematical Sciences. He has an M.A. and the Diploma in Mathematical Statistics from Cambridge University, England. He obtained his Ph.D. from Bath University. He is a former Chairman of the U.K. Simulation Society, a former Fellow of the Royal Statistical Society and a Fellow of the Institute of Mathematics and Its Applications. His research interests include: design and analysis of simulation experiments and parametric estimation methods. He was a Joint Founding Editor of the IMA Journal of Management Mathematics. He was awarded the INFORMS Simulation Society Lifetime Professional Achievement Award in 2016. His email address is cheng@btinternet.com.

The seminar will also be available via a live web-cast at
https://coursecast.soton.ac.uk/Panopto/Pages/Viewer.aspx?id=cd1558e8-fb27-4238-bb5a-9aa3aba39646