Sentence examples for model input variables from inspiring English sources
Uncertainty and variability in model input variables were represented using probability distributions, and uncertainty in the model output was estimated through statistical simulation.
An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale.
It may occur in practice that some model input variables and parameters can be represented by probability distributions (due to observed variability and sufficient statistics), while others are better represented by possibility distributions (due to imprecision), or by the Dempster Shafer belief functions (due to partial observed variability and partial ignorance).
We also estimated uncertainties of the model input variables based on values reported in the literature.
Notably, the model input variables can be easily calculated for non-specialist user.
The ANN model input variables are: CPT tip resistance (qc), effective vertical stress (σ′v), and CPT sleeve friction (fs).
Thirdly, Edgeworth expansion is employed to approximate the failure-conditional PDF of each model input variable.
The calibration planning activity identifies vital tests and predictive models along with both known and unknown model input variable(s).
The global reliability sensitivity analysis measures the effect of each model input variable on the failure probability, which is very useful for reliability-based optimization design.
Firstly, Bayes formula is used to convert the original expression of global reliability sensitivity index into an equivalent form where only the unconditional failure probability and the failure-conditional probability density function (PDF) of each model input variable are required.
In the proposed procedure, subset simulation is used to estimate the unconditional failure probability, and Metropolis-Hastings algorithm is employed to convert the samples in failure domain from the current PDF in subset simulation to the PDF corresponding to the original PDF of model inputs for estimating the failure-conditional PDF of each model input variable.
