Reducing and Calibrating for Input Model Bias in Computer Simulation

DOI10.1287/ijoc.2022.1183OpenAlexW4220654441MaRDI QIDQ5106428

Russell R. Barton, Lucy E. Morgan, Luke Rhodes-Leader

Publication date: 19 September 2022

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.2022.1183

zbMATH Keywords

simulationbias reductioninput modelling error

Mathematics Subject Classification ID

Mathematical programming (90Cxx)

Uses Software

• VBASim
• bootstrap
• GitHub

Cites Work

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• On R-linear convergence of semi-monotonic inexact augmented Lagrangians for bound and equality constrained quadratic programming problems with application
• Some consequences of estimating parameters for the M/M/1 queue
• Towards an efficient augmented Lagrangian method for convex quadratic programming
• Bayesian Calibration of Computer Models
• Using Experts’ Noisy Quantile Judgments to Quantify Risks: Theory and Application to Agribusiness
• Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses
• Reducing Simulation Input-Model Risk via Input Model Averaging
• Foundations and methods of stochastic simulation. A first course

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