scholarly journals Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

2018 ◽  
Vol 35 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Olivia Eriksson ◽  
Alexandra Jauhiainen ◽  
Sara Maad Sasane ◽  
Andrei Kramer ◽  
Anu G Nair ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Analysis ◽  
Uncertainty Quantification ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
Pathway Models ◽  
Intracellular Pathway

scholarly journals Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models

2018 ◽  
Author(s):  
Olivia Eriksson ◽  
Alexandra Jauhiainen ◽  
Sara Maad Sasane ◽  
Andrei Kramer ◽  
Anu G Nair ◽  
...  
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Bayesian Analysis ◽  
Uncertainty Quantification ◽  
Posterior Distribution ◽  
Global Sensitivity Analysis ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Individual Parameter ◽  
Global Sensitivity

AbstractMotivationDynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as give guidance on parameters that are essential in distinguishing different qualitative output behaviours.ResultsWe used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a global sensitivity analysis was performed on the prediction using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large and complex model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions.This approach is useful both for experimental design as well as model building.

Uncertainty quantification and global sensitivity analysis with dependent inputs: Application to the 2D hydraulic model of the Loire River

2020 ◽  
Author(s):  
Lucie Pheulpin ◽  
Vito Bacchi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Large Scale ◽  
Global Sensitivity Analysis ◽  
Numerical Models ◽  
Hydraulic Model ◽  
Model Parameters ◽  
Traditional Methods ◽  
Global Sensitivity ◽  
Loire River

<p>Hydraulic models are increasingly used to assess the flooding hazard. However, all numerical models are affected by uncertainties, related to model parameters, which can be quantified through Uncertainty Quantification (UQ) and Global Sensitivity Analysis (GSA). In traditional methods of UQ and GSA, the input parameters of the numerical models are considered to be independent which is actually rarely the case. The objective of this work is to proceed with UQ and GSA methods considering dependent inputs and comparing different methodologies. At our knowledge, there is no such application in the field of 2D hydraulic modelling.</p><p>At first the uncertain parameters of the hydraulic model are classified in groups of dependent parameters. Within this aim, it is then necessary to define the copulas that better represent these groups. Finally UQ and GSA based on copulas are performed. The proposed methodology is applied to the large scale 2D hydraulic model of the Loire River. However, as the model computation is high time-consuming, we used a meta-model instead of the initial model. We compared the results coming from the traditional methods of UQ and GSA (<em>i.e.</em> without taking into account the dependencies between inputs) and the ones coming from the new methods based on copulas. The results show that the dependence between inputs should not always be neglected in UQ and GSA.</p>

scholarly journals Uncertainty quantification and global sensitivity analysis for economic models

10.3982/qe866 ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 1-41 ◽  
Author(s):  
Daniel Harenberg ◽  
Stefano Marelli ◽  
Bruno Sudret ◽  
Viktor Winschel
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Global Sensitivity Analysis ◽  
Economic Models ◽  
Global Sensitivity
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