Sentence examples for variation assumptions from inspiring English sources

The proposed approach is free from small variation assumptions, so that it is suitable for handling even the case of large error corrections.

Given the variation in assumptions in the literature, a fundamental question needs to be addressed: what exactly do we mean by spatial thinking?

Despite this, and given that the overall results were most sensitive to variation in assumptions regarding the costs associated with physician consultation, intensive treatment became dominant (cost- and lifesaving) compared with conventional therapy when it was assumed that patients in both treatment arms received care in a primary care setting.

The effect of variation in assumptions about the time to sputum conversion in (pre- XDR, about error rates of diagnostic pre- XDRes (contaboutterrorlturatestc)., and of increasediagnosticnd time by 2 weeks (e.g. due to transproceduresof specontaminatedsmall and culturesthetcumber of PNTPM on the order of 2–8 % compared to the primary analysis.

In studies of natural variation, this assumption does not always hold, raising a number of new challenges.

Sensitivity analyses were performed to assess the impact of variations in assumptions of key model parameters on the economic outcome.

If the model holds, item parameter estimates do not differ across subsamples but for random variation (invariability assumption, cf. Engelhard Jr. 2013).

Sensitivity analyses making variations in assumptions, proved the robustness of the results in this paper.

A number of variations in assumptions were tested and were found to have little impact on the overall conclusions (Table 4).

There are also variations in assumptions about the precision required for point location, as for some clinicians and investigators acupuncture points are considered as areas of reactivity rather than points of action.

Variations in assumptions regarding costs and outcomes were examined in order to establish the strongest result drivers of the model and to verify the robustness of the primary results.