Sentence examples for models two sources from inspiring English sources

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In random effect models, two sources of error variability are taken into account: within-study variability and between-study variability.

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HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields.

Finally, we modelled two sources of ROS that might explain the effects of LY30 on caspase-8 activation, and that might be relevant for other anti-cancer drugs.

In this model, two source compartments were used: E) 211At homogeneously distributed within the surrounding follicle lumens, and F) 211At homogeneously distributed within the surrounding follicle cells.

When future cancer incidence is predicted in a statistical model, three sources of uncertainty are associated with the predicted numbers.

Therefore, in our model three sources of fishing mortality are considered (Post et al. 2003): harvest mortality of fish exceeding the MSL, hooking mortality of fish caught undersized and then released, and noncompliance mortality from illegal harvest of undersized fish.

A simple model identifies two sources of inefficienc: hospitals do not internalize their patients' benefits and current mechanisms sub-optimally rewards hospitals for their submissions of patients and donors.

As stated, dual modeling from two sources prevents the method from losing track whenever one of the sources is unreliable.

Using predictions rather than true exposures in health modeling introduces two sources of measurement error that potentially influence the behavior of ^βx.

From the transformed data, maximum likelihood summary relative risks (SRR) were calculated using a random effects model including two sources of variation (between study variance and, when applicable, within study variance; van Houwelingen et al, 2002).

The combination of a generative and discriminative model on two sources of information provides a more reliable and accurate method for nuclei detection: although each individual distribution is prone to its own false-positives and false-negatives, by combining the two approaches we can provide a more accurate method of detection.

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