Reconstructing input models via simulation optimization
Aleksandrina Goeva,Henry Lam,Bo Zhang +2 more
- 07 Dec 2014
- pp 698-709
TL;DR: This paper takes a nonparametric viewpoint, and forms this inverse problem as a stochastic program by maximizing the entropy of the input distribution subject to moment matching, and proposes an iterative scheme via simulation to approximately solve the program.
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Abstract: In some service operations settings, data are available only for system outputs but not the constituent input models Examples are service call centers and patient flows in clinics, where sometimes only the waiting time or the queue length data are collected for economic or operational reasons, and the data on the "input distributions", namely interarrival and service times, are limited or unavailable In this paper, we study the problem of estimating these input distributions with only the availability of the output data, a problem usually known as the inverse problem, and we are interested in the context where stochastic simulation is required to generate the outputs We take a nonparametric viewpoint, and formulate this inverse problem as a stochastic program by maximizing the entropy of the input distribution subject to moment matching We then propose an iterative scheme via simulation to approximately solve the program
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The design and analysis of computer experiments
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Inverse Problem Theory and Methods for Model Parameter Estimation
Albert Tarantola
- 20 Dec 2004
TL;DR: This chapter discusses Monte Carol methods, the least-absolute values criterion and the minimax criterion, and their applications to functional inverse problems.
Bayesian Calibration of computer models
Marc C. Kennedy,Anthony O'Hagan +1 more
TL;DR: A Bayesian calibration technique which improves on this traditional approach in two respects and attempts to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best‐fitting parameter values is presented.
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Design and analysis of computer experiments
Sonja Kuhnt,David M. Steinberg +1 more
TL;DR: The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.
Design and Analysis of Computer Experiments
Magnus Arnér
- 07 Mar 2014
TL;DR: It is concluded that the strategy as proposed by Sacks and coworkers is not suited for implementation in a design optimization tool, mainly because of two reasons: maximum likelihood parameter estimation is computationally expensive and not straightforward, while the quality of the parameter estimations is questionable.