Sentence examples for this approximation resulted from inspiring English sources

This approximation resulted in an underestimation of lesion volumes.

This approximation results in a geometric program that can be efficiently solved.

Obviously, this approximation results in a suboptimal detection scheme, but it is well known that it does not exist in closed-form ML estimators for Toeplitz matrices.

This approximation results in a closed-form formula with complexity similar to IWF (and lower than NF-IWF) but improved performance as we will see later.

This approximation results in a setup time, S, larger than its real value, which provides an upper bound in (59) that can be sometimes looser than expected.

However, applying this approximation results in discrete values of Dk(t) rather than a continuous curve (results not shown).

However, the sample sizes using this approximation result in differences in sample sizes compared with exact methods, particularly for relatively small studies.

Cuboid approximation resulted in approximately +50 % overestimation of lesion volumes.

Ellipsoid approximation resulted in an average underestimation of tumor volume by -8 % and of largest suspect LN volume by −18%%.

Michaelis-Menten (MM) approximations resulted in drug PK comparable to the full TMDD model for both age groups, particularly at the high concentration range.

As an alternative we use a variable density low Mach number model (see [8 12] for the non isentropic or the non isothermal cases) adapted to this approximation which results to be realistic and numerically cheap at the same time.