Sentence examples for partial sensitivity analysis from inspiring English sources

The results of the partial sensitivity analysis are presented in Table 7.

In a partial sensitivity analysis one input variable is selected and its values are changed while holding the values of the other input variables constant.

We performed a partial sensitivity analysis using the input variables (1) logging residuals, (2) displacement factor, (3) energy from logging residuals, (4) energy from HWPs, and (5) type of fossil fuel for substitution minor and studied their effect on CO2 emissions.

We used a combination of one way and (where appropriate) partial multi-way sensitivity analysis to determine how sensitive the findings of this modelled analysis were to various assumptions.

Partial identification and sensitivity analysis an important class of techniques that are well-suited for providing causal inferences about Gormley et al.'s and other important research questions that are impeded by endogeneity concerns.

These are developed from a detailed microkinetic model using a computer-aided model reduction strategy that employs reaction path analysis, sensitivity analysis, partial equilibrium analysis, and simple algebra to deduce the most abundant reaction intermediate and the rate-determining step.

We implemented the six steps of model design, process level calibration, qualitative evaluation, quantitative evaluation, global sensitivity analysis, and partial recalibration using approximate Bayesian computing.

This paper also shows that a sensitivity analysis through partial derivatives may not capture the real level of impact for some parameters in a discrete domain, such as the number of active servers.

In metrology, Monte-Carlo Methods are used to evaluate the measurement uncertainty whereas metrologists were used to apply a Taylor series approximation and to perform a local sensitivity analysis with the partial derivatives.

Most local sensitivity analysis methods use partial derivatives evaluated at a point in parameter space to determine how the model output changes locally with respect to small variations of a particular parameter [ 11, 13, 21, 22].

We determined the quantitative effect of each of these three variables on the cumulative percentage of TB cases prevented by performing a multivariate sensitivity analysis and calculating partial rank correlation coefficients (PRCCs) (Appendix).