Sentence examples for strength of path from inspiring English sources

Technically, sample size for an SEM varies depending on many factors, including fit index, model size, distribution of the variables, amount of missing data, reliability of the variables, and strength of path parameters (Fan et al. 1999; Muthen and Muthen 2002; Fritz and MacKinnon 2007; Iacobucci 2010).

p_{ij} = frac{{left[ {tau_{ij} } right]^{alpha } left[ {eta_{ij} } right]^{beta } }}{{sumlimits_{x in allowed} {left[ {tau_{ij} } right]^{alpha } left[ {eta_{ij} } right]^{beta } } }} (6 where p ij is the probability value to select the next city; α is the pheromone trail constant; β is the guide investigation constant; τ ij is the pheromone strength of path between city i to j.

To statistically compare the strength of path representation in the replays between selected regions, path representation strength was defined as the sum total of the posterior probability.

Likewise, we also take into consideration the strength of paths: the strength of the effect via longer paths with more intermediate vertices is very likely to be lower than those via shorter ones with fewer intermediaries.

Using this approach, significant results indicate a meaningful difference in the strength of the path(s) tested between the groups (regardless of individual path statistical significance in either group).

If the model fit when the path is unconstrained is significantly improved, as determined by a χ difference test, this indicates that the strength of that path differs among the groups (Protzner & McIntosh, 2006).

Rayleigh fading is assumed for NLOS scenarios, where the signal strength of each path follows the Rayleigh distributions [36, 37].

Note the above method performs well in dense multipath environments, especially in non-line-of-sight conditions[2, 38 41], where the strength of direct path is significantly lower than the LPs.

With this algorithm, voxels having no label were assigned probabilities based on the random walkers' computed probability, measuring the strength of the path initiated from labeled signatures to reach the unlabeled voxels first.

On the other hand, the fact that the strength of the path coefficient from L2 linguistic knowledge to L2 listening proficiency was the highest of all provides evidence for the relative importance of L2 linguistic knowledge for L2 listening proficiency, and this appears to implicate the somewhat controversial notion of a linguistic threshold.

In addition, the true strength of the path tree data type is its powerful regular-expressions-like query syntax in combination with GiST index support.