B
Bahar Biller
Researcher at General Electric
Publications - 43
Citations - 680
Bahar Biller is an academic researcher from General Electric. The author has contributed to research in topics: Stochastic simulation & Independent and identically distributed random variables. The author has an hindex of 13, co-authored 41 publications receiving 628 citations. Previous affiliations of Bahar Biller include SAS Institute & Carnegie Mellon University.
Papers
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Modeling and generating multivariate time-series input processes using a vector autoregressive technique
Bahar Biller,Barry L. Nelson +1 more
TL;DR: The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that the authors presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution.
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Fitting Time-Series Input Processes for Simulation
Bahar Biller,Barry L. Nelson +1 more
TL;DR: An automated and statistically valid algorithm is presented to fit autoregressive-to-anything (ARTA) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data.
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Accounting for Parameter Uncertainty in Large-Scale Stochastic Simulations with Correlated Inputs
Bahar Biller,Canan G. Corlu +1 more
TL;DR: The Bayesian model is incorporated into the simulation replication algorithm for the joint representation of stochastic uncertainty and parameter uncertainty in the mean performance estimate and the confidence interval and shows that the model improves both the consistency of the mean line-item fill-rate estimates and the coverage of the confidence intervals in multiproduct inventory simulations with correlated demands.
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Improved Inventory Targets in the Presence of Limited Historical Demand Data
TL;DR: This paper considers a repeated newsvendor setting where this is not the case and studies the problem of setting inventory targets when there is a limited amount of historical demand data, to quantify the inaccuracy in the inventory-target estimation as a function of the length of the historicalDemand data, the critical fractile, and the shape parameters of the demand distribution.
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Copula-Based Multivariate Input Models for Stochastic Simulation
TL;DR: A copula-based multivariate time-series input model, which includes VARTA as a special case, allows the development of statistically valid fitting and fast sampling algorithms well suited for driving large-scale stochastic simulations.