Sentence examples for multivariate uncertainty analysis from inspiring English sources

It is shown that the multivariate uncertainty analysis method fails when the perturbations are relatively large and/or the output distribution is non-Gaussian, and therefore the Monte-Carlo analysis should be used in the general case.

Sensitivity analysis and multivariate uncertainty analysis are performed, conform current Dutch standards for pharmaco-economic research [ 23].

Multivariate uncertainty analysis supports the findings, showing that at a threshold of 35,000 USD, 2VCT, AZT+NVP is preferable to other VCT and drug strategies.

The assessment has proved by both point estimate and multivariate uncertainty analysis that the programme D has a lower cost per paediatric HIV infection averted than other alternatives.

To do this, we performed a multivariate uncertainty analysis by running the model with 1000 randomly sampled parameter values from the uncertainty distributions for the antiviral treatment SVR and the efficacy of OST and HCNSP on reducing HCV transmission risk (Table 1).

This was followed by a multivariate uncertainty analysis in which the simulation was repeated 10 000 times by Monte Carlo sampling from the uncertainty ranges around each parameter in the table and across the risk factor distributions listed in the appendix, generating 95% confidence intervals around the model's results.

To consider the effect of uncertainty in the underlying parameters, we performed a multivariate probabilistic uncertainty analysis where 1,000 parameter sets were randomly sampled from setting specific parameter distributions in Table 1.

Then we conducted multivariate uncertainty and sensitivity analysis to determine the uncertainty in the performance measure that was due to the uncertainty in estimating the input parameters.

A multivariate uncertainty and sensitivity analysis was performed to study the effects of input parameters on the control policy for the linear cost model.

Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis.

The multivariate technique makes it possible to obtain an uncertainty analysis for the possibly correlated real and imaginary parts of the complex quantities.