Sentence examples for differing random from inspiring English sources

Exact(2)

Comparison of models with differing random effects was done using a restricted maximum likelihood approach, while a maximum likelihood approach was used for comparing models with differing fixed effects [17].

For comparing multivariate models with differing random effects, we (conservatively) set the degrees of freedom to equal the number of additional (co variance parameters estimated in the more complex model.

Similar(58)

Tremor voices from patients with disorders such as paresis, Parkinson's disease, hyperfunction, and adductor spasmodic dysphonia show low-dimensional characteristics, differing from random noise.

Finally a redundancy criterion is applied to each variable position in the family alignment before a sequence is validated as a separate gene sequence thus reducing the inclusion of sequences differing by random cloning or sequencing errors as separate gene sequences.

Extension products were purified on Sephacryl S-400 HR spin columns (GE Healthcare, Chalfont St. Giles, UK) and mixed with "random primer B", which differed from "random primer A" by having a different fixed sequence B (also containing a Bgl II restriction site).

> As in the Wood pigeon data, the overall frequency of the four different brood sex compositions did not differ from random (G3 = 4.80, p = 0.10, Table  3).

For example, if one only uses random English word pairs as a False relation, then the system may incorrectly say all co-occurring genes are true relations due to random gene gene pairs existing with an easily recognizable genetic context that differs from random English words.

To determine how the size of the intersection differed from random, we repeated the above analysis 100 times using randomized P-values.

The combination of a rank 1 male nestling with a rank 2 female occurred most frequently (Table  1), but the overall frequency of the four different brood compositions with respect to sex of the siblings did not differ from random expectance (G3 = 4.39, p = 0.22).

Random-effects meta-analysis, on the other hand, assumes that estimated study effects are not identical; but differ at random, following a normal distribution [ 14].

The results of these solutions will differ if random noise is considered in the SGG data.

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