[PDF] Top 20 Global sensitivity analysis of key parameters in a process-based sugarcane growth model: a Bayesian approach
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Global sensitivity analysis of key parameters in a process-based sugarcane growth model: a Bayesian approach
... analyse model sensitivity, their application to complex process-based models is often impractical due to the large number of simulation runs ...A Bayesian approach to ... See full document
8
Bayesian statistical calibration of variety parameters in a sugarcane crop model
... analyse model sensitivity, their application to complex process‐based models is often impractical due to the large number of simulation runs ...A Bayesian approach to ... See full document
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Optimization of Process Parameters of Global Sequence Alignment Based Dynamic Program an Approach to Enhance the Sensitivity of Alignment
... the analysis of identity and ...the sensitivity and specificity of the alignment, computational time, cost, configuration of the hardware ...is based on dynamic programming for the enhancement of the ... See full document
6
Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators
... construct. Sensitivity analysis can be used to rank model parameters by their effect on outputs, and to quantify how uncertainty in parameters influences output ...of analysis is ... See full document
21
Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.
... many parameters, and how uncertainties in these parameters affect the model output is difficult to assess without undertaking large numbers of model ...statistical model of a cardiac ... See full document
21
Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters
... Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison ... See full document
21
Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach
... the analysis such as the specific experiment design, experiment length, and initial ...the model itself, and scores are more difficult to predict with a smooth Gaussian ...alternative approach ... See full document
20
SimSphere model sensitivity analysis towards establishing its use for deriving key parameters characterising land surface interactions
... estimate parameters char- acterising land surface interactions is currently a key scien- tific priority due to their central role in the Earth’s global energy and water ...been based on ... See full document
15
A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters
... the model-independent measurements L, µ max and A we derive from the fitted curves allow an immediate comparison of key values between different time series, even if qualitatively different models were used ... See full document
24
A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters
... the model-independent measurements L, µ max and A we derive from the fitted curves allow an immediate comparison of key values between different time series, even if qualitatively different models were used ... See full document
25
Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined
... CONCLUSIONS Global sensitivity analysis is an interesting tool for ranking parameters with respect to their contribution to the variance of the output variables of a ...of sensitivity ... See full document
13
Sensitivity analysis of key operating parameters of combine harvesters
... The sensitivity analysis of key operating parameters on the average annual sub-profit in a group of three combine har- vesters operating in companies providing agricultural services were ... See full document
9
Correction: Bayesian Sensitivity Analysis of a Cardiac Cell Model Using a Gaussian Process Emulator.
... In this supporting information, we include details of the mathematics that underpin the approach taken in this study. A much more in-depth coverage of Gaussian process (GP) emulators is given in the MUCM ... See full document
10
Sensitivity analysis of cotton trade liberalization: a global simulation model approach
... With regards to the remaining countries, all the market prices of cotton originating from the non-subsidizing countries show the positive change as expected. However, the level of such changes is still lower than that of ... See full document
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Hierarchical Priors for Bias Parameters in Bayesian Sensitivity Analysis for Unmeasured Confounding
... We model the joint distribution of (X, C , U ) using a loglinear model with pairwise ...logistic model for unmeasured confounding that is identical to that of McCandless et ...the model, prior ... See full document
28
A global sensitivity analysis approach for morphogenesis models.
... traditional analysis tools in dynamical systems the- ory, such as bifurcation analysis and phase plane analysis, fall ...some parameters is overlooked: the conclusions may depend on what sets ... See full document
15
Forecast Combination and Bayesian Model Averaging - A Prior Sensitivity Analysis
... Hereby model selection ignores an important source of risk: model ...of Bayesian model averag- ing is rooted in the statistically sound way model uncertainty is ...best model ... See full document
20
Toward a more robust variance-based global sensitivity analysis of model outputs
... the model output variance is possible (for independent input ...complex model functions. When the model is purely linear, the Sobol’ indices are equivalent to the standardized regression coefficient ... See full document
20
A Supervised Feature Selection Approach Based on Global Sensitivity
... on Global Sensitivity Hana Sulieman and Ayman Alzaatreh Abstract In this paper we propose a wrapper method for feature selection in supervised ...is based on the global sensitivity ... See full document
13
A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model
... of parameters with relatively weak influence on the outputs used in ...of model error as described by Kennedy and O’Hagan ...of model limitation and may be used to identify a source of error in the ... See full document
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