Stochastic simulation uncertainty analysis to accelerate flexible biomanufacturing process development
Motivated by critical challenges and needs from biopharmaceuticals manufacturing, we
propose a general metamodel-assisted stochastic simulation uncertainty analysis framework …
propose a general metamodel-assisted stochastic simulation uncertainty analysis framework …
Statistically Optimal Uncertainty Quantification for Expensive Black-Box Models
Uncertainty quantification, by means of confidence interval (CI) construction, has been a
fundamental problem in statistics and also important in risk-aware decision-making. In this …
fundamental problem in statistics and also important in risk-aware decision-making. In this …
Fixed Budget Ranking and Selection with Streaming Input Data
We consider a fixed budget ranking and selection problem with input uncertainty, where
unknown input distributions can be estimated using input data arriving in batches of varying …
unknown input distributions can be estimated using input data arriving in batches of varying …
Quantifying Distributional Input Uncertainty via Inflated Kolmogorov-Smirnov Confidence Band
In stochastic simulation, input uncertainty refers to the propagation of the statistical noise in
calibrating input models to impact output accuracy, in addition to the Monte Carlo simulation …
calibrating input models to impact output accuracy, in addition to the Monte Carlo simulation …
Doubly robust stein-kernelized monte carlo estimator: Simultaneous bias-variance reduction and supercanonical convergence
Standard Monte Carlo computation is widely known to exhibit a canonical square-root
convergence speed in terms of sample size. Two recent techniques, one based on control …
convergence speed in terms of sample size. Two recent techniques, one based on control …
Distributional input uncertainty
The vast majority of the simulation input uncertainty literature focuses on estimating target
output quantities that are real-valued. However, outputs of simulation models are random …
output quantities that are real-valued. However, outputs of simulation models are random …
Propagation of Input Tail Uncertainty in Rare-Event Estimation: A Light versus Heavy Tail Dichotomy
We consider the estimation of small probabilities or other risk quantities associated with rare
but catastrophic events. In the model-based literature, much of the focus has been devoted …
but catastrophic events. In the model-based literature, much of the focus has been devoted …
Blackbox Simulation Optimization
H Cao, JQ Hu, T Lian - Journal of the Operations Research Society of …, 2024 - Springer
Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …
stochastic systems. The majority of the previous works on simulation optimization rely …
Efficient Input Uncertainty Quantification for Ratio Estimator
L He, B Feng, E Song - arXiv preprint arXiv:2410.04696, 2024 - arxiv.org
We study the construction of a confidence interval (CI) for a simulation output performance
measure that accounts for input uncertainty when the input models are estimated from finite …
measure that accounts for input uncertainty when the input models are estimated from finite …
[引用][C] Portfolio management of a small RES utility with a structural vector autoregressive model of electricity markets in Germany
K Maciejowska - Operations Research and …, 2022 - Oficyna Wydawnicza Politechniki …