Analyzing stochastic computer models: a review with opportunities
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Publication:2075795
DOI10.1214/21-STS822OpenAlexW3082154183MaRDI QIDQ2075795
Pierre Barbillon, Arindam Fadikar, Jerome Sacks, Leah R. Johnson, Anirban Mondal, Evan Baker, Vadim Sokolov, Jiangeng Huang, Radu Herbei, Robert B. Gramacy, Bianica Pires, Pulong Ma, David M. Higdon
Publication date: 16 February 2022
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.01321
emulatorGaussian processcalibrationuncertainty quantificationcomputer experimentsurrogatescomputer modelagent based model
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Cites Work
- Unnamed Item
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- Unnamed Item
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- Unnamed Item
- BART: Bayesian additive regression trees
- Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction
- Microsimulation Model Calibration using Incremental Mixture Approximate Bayesian Computation
- Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
- Estimating the health impact of climate change with calibrated climate model output
- Classification and categorical inputs with treed Gaussian process models
- Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models
- Design of computer experiments: space filling and beyond
- Global sensitivity analysis of stochastic computer models with joint metamodels
- A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry
- Efficient calibration for imperfect computer models
- Probabilistic projections of HIV prevalence using Bayesian melding
- Kriging metamodeling in simulation: a review
- Global optimization of expensive-to-evaluate functions: An empirical comparison of two sampling criteria
- Bayesian emulation of complex multi-output and dynamic computer models
- Efficient global optimization of expensive black-box functions
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package
- Optimal design for correlated processes with input-dependent noise
- Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study
- Nested kriging predictions for datasets with a large number of observations
- The design and analysis of computer experiments
- Regression and Kriging metamodels with their experimental designs in simulation: a review
- Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise
- Analysis methods for computer experiments: how to assess and what counts?
- INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles
- Bayesian learning for neural networks
- Calibration of computer models with multivariate output
- Composite Gaussian process models for emulating expensive functions
- Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation
- Analyzing stochastic computer models: a review with opportunities
- Batch sequential designs for computer experiments
- Extended \(t\)-process regression models
- Parameter inference for an individual based model of chytridiomycosis in frogs
- Computer model validation with functional output
- Galaxy formation: a Bayesian uncertainty analysis
- Bayesian Calibration of Computer Models
- Learning about physical parameters: the importance of model discrepancy
- Automating Emulator Construction for Geophysical Hazard Maps
- Introduction to Uncertainty Quantification
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
- Stochastic Kriging for Simulation Metamodeling
- Comparison of Statistical and Deterministic Frameworks of Uncertainty Quantification
- Heteroscedastic BART via Multiplicative Regression Trees
- Computer Model Calibration Using High-Dimensional Output
- Regression Quantiles
- Kriging the quantile: application to a simple transmission line model
- Fisher information and maximum-likelihood estimation of covariance parameters in Gaussian stochastic processes
- Inference for Deterministic Simulation Models: The Bayesian Melding Approach
- Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation
- Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model
- Combining Field Data and Computer Simulations for Calibration and Prediction
- Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
- Inference from a Deterministic Population Dynamics Model for Bowhead Whales
- Predicting the output from a complex computer code when fast approximations are available
- Synthesizing simulation and field data of solar irradiance
- VPS: VORONOI PIECEWISE SURROGATE MODELS FOR HIGH-DIMENSIONAL DATA FITTING
- Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
- Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
- Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes
- Statistics for Spatial Data
- Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data
- Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons
- Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Efficient Computation of Sobol' Indices for Stochastic Models
- Space-Time Approach to Non-Relativistic Quantum Mechanics
- On the distribution of points in a cube and the approximate evaluation of integrals
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
- A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties
- Emulators for stochastic simulation codes
- Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes
- On Distributions of Certain Wiener Functionals
- Modularization in Bayesian analysis, with emphasis on analysis of computer models
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