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Active learning for efficiently training emulators of computationally expensive mathematical models

https://doi.org/10.1002/sim.8679
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Serum Prostate-Specific Antigen in a Community-Based Population of Healthy Men
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Recommendations for Conduct, Methodological Practices, and Reporting of Cost-effectiveness Analyses
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Serum prostate-specific antigen in a community-based population of healthy men. Establishment of age-specific reference ranges
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Expected population impacts of discontinued prostate‐specific antigen screening
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The Design and Analysis of Computer Experiments
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The Role of the Nugget Term in the Gaussian Process Method
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Comparative studies of metamodelling techniques under multiple modelling criteria
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A benchmark of kriging-based infill criteria for noisy optimization
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Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening
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Cases for the nugget in modeling computer experiments
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Design of computer experiments: space filling and beyond
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Minimax and maximin distance designs
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OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution
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Gaussian processes and limiting linear models
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Multivariate versus univariate Kriging metamodels for multi-response simulation models
inferredcites →https://doi.org/10.1016/j.jspi.2009.08.006openalex-snapshot
Bayesian emulation of complex multi-output and dynamic computer models
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Bayesian analysis of computer code outputs: A tutorial
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Case studies in Gaussian process modelling of computer codes
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Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty
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Small response surface designs for metamodel estimation
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A methodology for fitting and validating metamodels in simulation1Two anonymous referees' comments on the first draft lead to an improved organization of our paper.1
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Archimedes: a new model for simulating health care systems—the mathematical formulation
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Efficient Global Optimization of Expensive Black-Box Functions
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Application-driven sequential designs for simulation experiments: Kriging metamodelling
inferredcites →https://doi.org/10.1080/10618600.2018.1458625openalex-snapshot
Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
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Cost-Effectiveness in Health and Medicine
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Maximum projection designs for computer experiments
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Calibrating disease progression models using population data: a critical precursor to policy development in cancer control
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Chapter 6: Modeling the Impact of Treatment and Screening on U.S. Breast Cancer Mortality: A Bayesian Approach
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Cost-Effectiveness in Health and Medicine
inferredcites →https://doi.org/10.1137/17m1157702openalex-snapshot
Coupling Computer Models through Linking Their Statistical Emulators
inferredcites →https://doi.org/10.1162/neco.1992.4.4.590openalex-snapshot
Information-Based Objective Functions for Active Data Selection
inferredcites →https://doi.org/10.1177/003754977502400606openalex-snapshot
The construction and implementation of metamodels
inferredcites →https://doi.org/10.1177/0272989x15578125openalex-snapshot
Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling
inferredcites →https://doi.org/10.1177/0272989x18798833openalex-snapshot
Future Directions for Cost-effectiveness Analyses in Health and Medicine
inferredcites →https://doi.org/10.1177/0272989x19837631openalex-snapshot
Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators
inferredcites →https://doi.org/10.1198/016214508000000689openalex-snapshot
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
inferredcites →https://doi.org/10.1198/jasa.2009.ap07466openalex-snapshot
Bayesian Calibration of Microsimulation Models
inferredcites →https://doi.org/10.1198/tech.2010.09157openalex-snapshot
Generalized Latin Hypercube Design for Computer Experiments
inferredcites →https://doi.org/10.1198/tech.2011.09141openalex-snapshot
A Computationally Stable Approach to Gaussian Process Interpolation of Deterministic Computer Simulation Data
inferredcites →https://doi.org/10.1214/14-sts487openalex-snapshot
Cross-Covariance Functions for Multivariate Geostatistics
inferredcites →https://doi.org/10.1214/16-aoas934openalex-snapshot
Parallel partial Gaussian process emulation for computer models with massive output
inferredcites →https://doi.org/10.1214/ss/1177012413openalex-snapshot
Design and Analysis of Computer Experiments
inferredcites →https://doi.org/10.1287/mnsc.41.12.1935openalex-snapshot
Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation
inferredcites →https://doi.org/10.1287/opre.50.6.981.347openalex-snapshot
D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve
inferredcites →https://doi.org/10.18637/jss.v019.i09openalex-snapshot
tgp: AnRPackage for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models
inferredcites →https://doi.org/10.18637/jss.v033.i06openalex-snapshot
Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering withtgpVersion 2, anRPackage for Treed Gaussian Process Models
inferredcites →https://doi.org/10.18637/jss.v064.i12openalex-snapshot
GPfit: AnRPackage for Fitting a Gaussian Process Model to Deterministic Simulator Outputs
inferredcites →https://doi.org/10.2307/1268522openalex-snapshot
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
inferredcites →https://doi.org/10.2307/1271432openalex-snapshot
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
inferredcites →https://doi.org/10.2307/3315868openalex-snapshot
Design and analysis of computer experiments when the output is highly correlated over the input space
inferredcites →https://doi.org/10.2337/diacare.26.11.3093openalex-snapshot
Archimedes
inferredcites →https://doi.org/10.2514/2.1185openalex-snapshot
Metamodeling of Combined Discrete/Continuous Responses
inferredcites →https://doi.org/10.2514/3.14954openalex-snapshot
Metamodeling of combined discrete/continuous responses
inferredcites →https://doi.org/10.7326/0003-4819-158-3-201302050-00003openalex-snapshot
Comparative Effectiveness of Alternative Prostate-Specific Antigen–Based Prostate Cancer Screening Strategies
inferredcites →https://doi.org/10.7326/m15-2531openalex-snapshot
Use of Decision Models in the Development of Evidence-Based Clinical Preventive Services Recommendations: Methods of the U.S. Preventive Services Task Force
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Gaussian Processes for Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An approach to quantify parameter uncertainty in early assessment of novel health technologies
inferred← citeshttps://doi.org/10.1002/sim.9195openalex-snapshot
Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision‐analytic modeling and cost‐effectiveness analysis
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inferred← citeshttps://doi.org/10.1177/0272989x241255618openalex-snapshot
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Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code
inferred← citeshttps://doi.org/10.3901/jme.2022.01.190openalex-snapshot
Meta-Learning Based Multi-Fidelity Deep Neural Networks Metamodel Method
inferred← citeshttps://doi.org/10.6084/m9.figshare.26673567openalex-snapshot
Additional file 1 of Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code
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