Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model

2010 ◽  
Vol 25 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Hubert Varella ◽  
Martine Guérif ◽  
Samuel Buis
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Global Sensitivity Analysis ◽  
Crop Model ◽  
Soil Parameters ◽  
Global Sensitivity

scholarly journals Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined

2012 ◽  
Vol 03 (07) ◽  
pp. 949-961 ◽  
Author(s):  
Hubert Varella ◽  
Samuel Buis ◽  
Marie Launay ◽  
Martine Guérif
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Crop Model ◽  
Soil Parameters ◽  
Global Sensitivity

scholarly journals Modelling HIV infection: model identification and global sensitivity analysis

2019 ◽  
Vol 14 (1) ◽  
pp. 19-33 ◽  
Author(s):  
V.V Zheltkova ◽  
D.A. Zheltkov ◽  
G.A. Bocharov
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Hiv Infection ◽  
Global Sensitivity Analysis ◽  
Immune Cell ◽  
Estimation Problem ◽  
Infection Model ◽  
Parameter Estimation Problem ◽  
Global Sensitivity ◽  
Systems Immunology

Mathematical modelling can be very useful in studying complex objects in modern systems immunology. In this work we studied the problem of modelling immune cell population dynamics for HIV infection through the set of models with different levels of complexity, which include several characteristics of HIV infection dynamics (antigen presenting, cell and humoral immune reactions, the effect of regulatory T-lymphocytes). We formulated and solved the parameter estimation problem using maximal likelihood approach, for two variants of modelling error quantifying. The global sensitivity analysis was implemented with LHS-PRCC method. Models were compared by the residual functional values and using the information-theoretical framework. We present the extended Marchuk-Petrov model for HIV infection with delays. For solving the parameter estimation problem for this model we compared a number of numerical optimization methods.

Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction

2006 ◽  
Vol 91 (10-11) ◽  
pp. 1142-1147 ◽  
Author(s):  
David Makowski ◽  
Cédric Naud ◽  
Marie-Hélène Jeuffroy ◽  
Aude Barbottin ◽  
Hervé Monod
Keyword(s):  
Sensitivity Analysis ◽  
Model Prediction ◽  
Genetic Parameters ◽  
Global Sensitivity Analysis ◽  
Crop Model ◽  
Global Sensitivity
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