Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018.
"Agent-based model calibration using machine learning surrogates,"
Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Working Papers hal-03458875, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01499344, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Papers 1703.10639, arXiv.org, revised Apr 2017.
- Frencesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-based model calibration using machine learning surrogates," Documents de Travail de l'OFCE 2017-09, Observatoire Francais des Conjonctures Economiques (OFCE).
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," LEM Papers Series 2017/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Working Papers hal-01499344, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," SciencePo Working papers Main hal-01499344, HAL.
- Mucahit Cevik & Mehmet Ali Ergun & Natasha K. Stout & Amy Trentham-Dietz & Mark Craven & Oguzhan Alagoz, 2016. "Using Active Learning for Speeding up Calibration in Simulation Models," Medical Decision Making, , vol. 36(5), pages 581-593, July.
- Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
- Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
- Bruce Edmonds & Christophe Le Page & Mike Bithell & Edmund Chattoe-Brown & Volker Grimm & Ruth Meyer & Cristina Montañola-Sales & Paul Ormerod & Hilton Root & Flaminio Squazzoni, 2019. "Different Modelling Purposes," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(3), pages 1-6.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Si Chen & Daniel Friedrich & Zhibin Yu & James Yu, 2019. "District Heating Network Demand Prediction Using a Physics-Based Energy Model with a Bayesian Approach for Parameter Calibration," Energies, MDPI, vol. 12(18), pages 1-19, September.
- Barde, Sylvain, 2024. "Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).
- Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
- Evan Baker & Peter Challenor & Matt Eames, 2021. "Future proofing a building design using history matching inspired level‐set techniques," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 335-350, March.
- Gyanendra Pokharel & Rob Deardon, 2022. "Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 455-479, March.
- Priscilla Avegliano & Jaime Simão Sichman, 2023. "Equation-Based Versus Agent-Based Models: Why Not Embrace Both for an Efficient Parameter Calibration?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-3.
- Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.
- Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
- Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
- Matteo Coronese & Davide Luzzati, 2022. "Economic impacts of natural hazards and complexity science: a critical review," LEM Papers Series 2022/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Barde, Sylvain, 2020.
"Macroeconomic simulation comparison with a multivariate extension of the Markov information criterion,"
Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
- Sylvain Barde, 2019. "Macroeconomic simulation comparison with a multivariate extension of the Markov Information Criterion," Studies in Economics 1908, School of Economics, University of Kent.
- Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
- Andrei I. Vlad & Alexei A. Romanyukha & Tatiana E. Sannikova, 2024. "Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms," Mathematics, MDPI, vol. 12(14), pages 1-21, July.
- Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
- Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017.
"Validation of Agent-Based Models in Economics and Finance,"
LEM Papers Series
2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2019. "Validation of Agent-Based Models in Economics and Finance," SciencePo Working papers Main halshs-02375423, HAL.
- Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2019. "Validation of Agent-Based Models in Economics and Finance," Post-Print halshs-02375423, HAL.
- Karl Naumann-Woleske & Max Sina Knicker & Michael Benzaquen & Jean-Philippe Bouchaud, 2022. "Exploration of the Parameter Space in Macroeconomic Models," Post-Print hal-03797418, HAL.
- Zila, Eric & Kukacka, Jiri, 2023. "Moment set selection for the SMM using simple machine learning," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 366-391.
- Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
- Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
- Edward Boone & Jan Hannig & Ryad Ghanam & Sujit Ghosh & Fabrizio Ruggeri & Serge Prudhomme, 2022. "Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study," Stats, MDPI, vol. 5(4), pages 1-17, November.
More about this item
Keywords
Agent-Based Modeling; Model Exploration; High-Performance Computing; Active Learning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2019-36-4. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.