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By utilizing the probability-based RANSAC, a higher successful rate can be obtained in G k (S r = 95%, see Table 6).
This method is extended and improved in the present paper by utilizing the concept of fractional probability weighted moments (FPWMs).
Furthermore, by utilizing the nonparametric statistical analysis, both probability density functions (PDFs) and cumulative distribution functions (CDFs) of concerned structural displacements and stresses can be effectively established.
WGAIM was extended for use in MAGIC populations by Verbyla et al. (2014), utilizing the probabilities of inheriting founder alleles for each individual at each locus.
Permutations were performed, utilizing the probabilities determined for each window, by placing 33 simulated reads across the viral genome.
The knowledge about the influencing quantities and parameters is expressed by their degree of belief, i.e. appropriate probability density functions that usually are obtained by utilizing the principle of maximum information entropy and the Bayes theorem.
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Thus, for each 1 day increase in the amount of monthly services utilized, the probability of change from CL1 dropped by an estimated 1.6% (data not shown).
Some papers utilize the probability of missed detection (P m) which is simply given by 1−P d.
ITS also utilizes the probability of target existence as a track quality measure.
For TargetScan [27] [29], we utilized the probability of preferentially conserved targeting (PCT), a Bayesian estimate of the probability of site conservation due to selective maintenance of miRNA targeting.
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