Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pcbi.1006181
Download full text from publisher
References listed on IDEAS
- W Andrew Pruett & Robert L Hester, 2016. "The Creation of Surrogate Models for Fast Estimation of Complex Model Outcomes," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-11, June.
- Cariboni, J. & Gatelli, D. & Liska, R. & Saltelli, A., 2007. "The role of sensitivity analysis in ecological modelling," Ecological Modelling, Elsevier, vol. 203(1), pages 167-182.
- Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
- Yohei Murakami, 2014. "Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-15, August.
- Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
- Li, Song & Tso, Geoffrey K.F. & Long, Lufan, 2017. "Powered embarrassing parallel MCMC sampling in Bayesian inference, a weighted average intuition," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 11-20.
- Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Joel A Paulson & Marc Martin-Casas & Ali Mesbah, 2019. "Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-35, August.
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.- Francisco A. Buendia-Hernandez & Maria J. Ortiz Bevia & Francisco J. Alvarez-Garcia & Antonio Ruizde Elvira, 2022. "Sensitivity of a Dynamic Model of Air Traffic Emissions to Technological and Environmental Factors," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
- Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
- Matieyendou Lamboni, 2018. "Global sensitivity analysis: a generalized, unbiased and optimal estimator of total-effect variance," Statistical Papers, Springer, vol. 59(1), pages 361-386, March.
- Wu, Zeping & Wang, Donghui & Okolo N, Patrick & Hu, Fan & Zhang, Weihua, 2016. "Global sensitivity analysis using a Gaussian Radial Basis Function metamodel," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 171-179.
- Wong, Chun Yui & Seshadri, Pranay & Parks, Geoffrey, 2021. "Extremum sensitivity analysis with polynomial Monte Carlo filtering," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
- Sudret, B. & Mai, C.V., 2015. "Computing derivative-based global sensitivity measures using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 241-250.
- Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
- Lamboni, Matieyendou, 2020. "Derivative-based generalized sensitivity indices and Sobol’ indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 236-256.
- Pannier, S. & Graf, W., 2015. "Sectional global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 110-117.
- Becker, William, 2020. "Metafunctions for benchmarking in sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
- Constantine, Paul G. & Diaz, Paul, 2017. "Global sensitivity metrics from active subspaces," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 1-13.
- Wu, Zeping & Wang, Wenjie & Wang, Donghui & Zhao, Kun & Zhang, Weihua, 2019. "Global sensitivity analysis using orthogonal augmented radial basis function," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 291-302.
- Konakli, Katerina & Sudret, Bruno, 2016. "Global sensitivity analysis using low-rank tensor approximations," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 64-83.
- Buzzard, Gregery T., 2012. "Global sensitivity analysis using sparse grid interpolation and polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 82-89.
- Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
- Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
- Pelletier, Dominique & Mahevas, Stéphanie & Drouineau, Hilaire & Vermard, Youen & Thebaud, Olivier & Guyader, Olivier & Poussin, Benjamin, 2009. "Evaluation of the bioeconomic sustainability of multi-species multi-fleet fisheries under a wide range of policy options using ISIS-Fish," Ecological Modelling, Elsevier, vol. 220(7), pages 1013-1033.
- S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
- Shang, Xiaobing & Wang, Lipeng & Fang, Hai & Lu, Lingyun & Zhang, Zhi, 2024. "Active Learning of Ensemble Polynomial Chaos Expansion Method for Global Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
- Frank H. Koch & Denys Yemshanov & Daniel W. McKenney & William D. Smith, 2009. "Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk," Risk Analysis, John Wiley & Sons, vol. 29(9), pages 1227-1241, September.
Corrections
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:plo:pcbi00:1006181. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.