Uncertainty quantification of stochastic simulation for black-box computer experiments
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Publication:1739334
DOI10.1007/s11009-017-9599-7zbMath1417.62039OpenAlexW2765267601MaRDI QIDQ1739334
Henry Lam, Eunshin Byon, Youngjun Choe
Publication date: 26 April 2019
Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11009-017-9599-7
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Uses Software
Cites Work
- Estimating the density of a conditional expectation
- Rare event probability estimation in the presence of epistemic uncertainty on input probability distribution parameters
- Saddlepoint approximations to the probability of ruin in finite time for the compound Poisson risk process perturbed by diffusion
- Testing the assumptions behind importance sampling
- An efficient algorithm for rare-event probability estimation, combinatorial optimization, and counting
- Adaptive Monte Carlo variance reduction for Lévy processes with two-time-scale stochastic approximation
- Controlled stratification for quantile estimation
- Estimating the dimension of a model
- Asymptotic normality of random fields of positively or negatively associated processes
- Rare events simulation for heavy-tailed distributions
- The cross-entropy method for combinatorial and continuous optimization
- Rare event simulation for a slotted time M/G/s model
- Bayesian spline method for assessing extreme loads on wind turbines
- The central limit theorem for dependent random variables
- Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation
- Importance Sampling for Stochastic Simulations
- Handbook of Monte Carlo Methods
- Stochastic Kriging for Simulation Metamodeling
- Importance Sampling for Portfolio Credit Risk
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Central Limit Theorems for dependent variables. I
- Large deviations theory and efficient simulation of excessive backlogs in a GI/GI/m queue
- A Note on the Central Limit Theorems for Dependent Random Variables
- Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement
- Fast simulation of rare events in queueing and reliability models
- Rare-Event Simulation for Many-Server Queues
- Methods of Reducing Sample Size in Monte Carlo Computations
- Contemporary Bayesian Econometrics and Statistics
- Generalized Additive Models for Location, Scale and Shape
- An introduction to statistical modeling of extreme values
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