Others have shown that eschewing traditional distribution models can result in big success, in comedy especially.
An incorrect selection of the parameters of the model can result in inaccurate simulation predictions.
Attempts to implement alternative staffing models can result in resistance as staff struggle to link work practice with patient numbers.
The simulation study shows that ignoring random effects in modeling can result in unreliable factors identification and estimation.
It is seen that performing co-segmentation based on the GrabCut model can result in efficient optimization and prior model generation.
The introduction of constraints in tensor models can result from the system itself that is under study or from a system design.
Further, Pelletier et al. [55] demonstrated that two different allometric models can result in estimated annual emissions from deforestation that differ by up to 48%.
Deviations from this simple model can result from numerous factors, such as contributions from surface anisotropy [5 7], interaction with an antiferromagnet [8 10] or a surface spin glass phase [11], or dipole dipole interactions [12, 13].
Furthermore, (independent) open learner models can result in significant learning gains and increased self-assessment accuracy (e.g. Brusilovsky et al. 2015; Kerly and Bull 2008; Long and Aleven 2013; Mitrovic and Martin 2007; Shahrour and Bull 2009).
