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Global Sensitivity Analysis of High-Dimensional Neuroscience Models: An Example of Neurovascular Coupling
11 references·2 citing·37 second hop
Global Sensitivity Analysis of High-Dimensional Neuroscience Models: An Example of Neurovascular Coupling (2019)Global Sensitivity Analysis of High-Dimen…Variance-based sensitivity analysis for time-dependent processes (2019)Variance-based sensitivity an…Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience (2018)Uncertainpy: A Python Toolbox…Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling (2018)Applying a Global Sensitivity…AN APPROXIMATION THEORETIC PERSPECTIVE OF SOBOL' INDICES WITH DEPENDENT VARIABLES (2018)AN APPROXIMATION THEORETIC PE…Introduction to Sensitivity Analysis (2017)Introduction to Sensitivity A…Efficient Computation of Sobol' Indices for Stochastic Models (2017)Efficient Computation of Sobo…UQLab: A Framework for Uncertainty Quantification in Matlab (2014)UQLab: A Framework for Uncert…Sobol' Indices and Shapley Value (2014)Sobol' Indices and Shapley Va…Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Variance based sensitivity an…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Introduction: Sensitivity Analysis (2006)Introduction: Sensitivity Ana…GLOBAL SENSITIVITY ANALYSIS OF RARE EVENT PROBABILITIES USING SUBSET SIMULATION AND POLYNOMIAL CHAOS EXPANSIONS (2022)GLOBAL SENSITIVITY ANALYSIS O…Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics (2020)Global Sensitivity Analysis: …Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)How to avoid a perfunctory sensitivity analysis (2010)Global sensitivity analysis using polynomial chaos expansions (2007)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis (2018)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)A value for n -person games (1988)Shapley Effects for Global Sensitivity Analysis: Theory and Computation (2016)On Shapley Value for Measuring Importance of Dependent Inputs (2017)Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications (2017)Stochastic Finite Elements: A Spectral Approach (1991)A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation (2021)A unified approach for global sensitivity analysis based on active subspace and Kriging (2021)An Introduction to Copulas (1999)ROBUSTNESS OF THE SOBOL' INDICES TO DISTRIBUTIONAL UNCERTAINTY (2019)COMPUTATION OF SOBOL INDICES IN GLOBAL SENSITIVITY ANALYSIS FROM SMALL DATA SETS BY PROBABILISTIC LEARNING ON MANIFOLDS (2020)Uncovering heterogeneous effects in computational models for sustainable decision-making (2023)Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)pksensi: An R package to apply global sensitivity analysis in physiologically based kinetic modeling (2020)Sensitivity analysis using Morris: Just screening or an effective ranking method? (2021)Strategic model reduction by analysing model sloppiness: A case study in coral calcification (2022)We Have Never Been Modern (1994)Merchants of doubt: how a handful of scientists obscured the truth on issues from tobacco smoke to global warming (2011)Basic Framework and Main Methods of Uncertainty Quantification (2020)Metafunctions for benchmarking in sensitivity analysis (2020)Power to Detect What? Considerations for Planning and Evaluating Sample Size (2023)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Global Sensitivity Analysis and Wasserstein Spaces (2021)Quantification and reduction of uncertainties in a wind turbine numerical model based on a global sensitivity analysis and a recursive Bayesian inference approach (2021)A dimension reduction-based Kriging modeling method for high-dimensional time-variant uncertainty propagation and global sensitivity analysis (2024)EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing (2020)UQpy: A general purpose Python package and development environment for uncertainty quantification (2020)Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows (2022)
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References

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  • 2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index3,567 cited
  • 2014UQLab: A Framework for Uncertainty Quantification in Matlab1,018 cited
  • 2014Sobol' Indices and Shapley Value268 cited
  • 2017Introduction to Sensitivity Analysis125 cited
  • 2018Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience111 cited
  • 2018Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling88 cited
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Cited by

  • 2020Global Sensitivity Analysis: a new generation of mighty estimators based on rank statistics12 cited
  • 2022GLOBAL SENSITIVITY ANALYSIS OF RARE EVENT PROBABILITIES USING SUBSET SIMULATION AND POLYNOMIAL CHAOS EXPANSIONS7 cited

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