Topic · Statistics, Probability and Uncertainty · Decision Sciences

Probabilistic and Robust Engineering Design

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Systems Biology·PLoS Computational BiologyPeer-reviewed·Jul 17, 2024
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CONVERGENCE, SAMPLING AND TOTAL ORDER ESTIMATOR EFFECTS ON PARAMETER ORTHOGONALITY IN GLOBAL SENSITIVITY ANALYSIS
Harry Saxton, Xu Xu, Torsten Schenkel…+1 more author·Ian Halliday
This paper evaluates total-order estimators and sampling methods in global sensitivity analysis, finding that Jansen and Janon estimators with Sobol and lattice rule sampling provide efficient convergence for parameter orthogonality.
6 citations
statistical model·benchmarking
·𝕏 Post·Bluesky
Neuroscience·Frontiers in NeuroinformaticsPeer-reviewed·Aug 14, 2018
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Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience.
Simen Tennøe, Geir Halnes, Gaute T. Einevoll
Uncertainpy provides a Python toolbox for uncertainty quantification and sensitivity analysis in neuroscience models, demonstrating its use on three case studies.
112 citationstop 2%
statistical model·benchmarking
·𝕏 Post·Bluesky