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To successfully and reliably align two scans when there is only a small overlap, we improve the basic RANSAC random selection step by employing a weight function that approximates the probability of each pair of points in one scan to match one pair in the other.
wherein R is the transmit data rate, Q≥1 is the packet length, D≤Q is the number of information symbols contained in each packet, γ is the achieved SINR, p the transmit power, and (1−e−γ) Q is the so-called efficiency function which approximates the probability of correct reception for a data-packet of length Q[4, 10, 30] and references therein.
It approximates the probability of ranking a randomly chosen positive instance higher than a randomly chosen negative one.
The threshold for the empirical P value corresponding to suggestive linkage was then calculated based on a family-wise error rate (FWER) of 1-e^{−1} = 0.632, this is the probability that a Poisson distributed random variable with an expected value of 1, is positive, and it approximates the probability of at least one significant linkage peak [ 29].
Genetic diversity within the tumour was quantified using three ecological measures: the Shannon index (a measure of the uncertainty in predicting the genotype of a sampled cell), the Simpson index (approximates the probability of drawing two cells that have the same genotype; smaller values indicate a more diverse population) and the richness (the number of different clones).
The amount of m a undergoing translation compared to the total m a is the relative frequency of loading and approximates the probability of ribosomal loading, i.e. (20a) π a ≈ f 1 = [ R 70 m a ] [ m a ] + [ R 70 m a ], where f l is the relative loading frequency and π a is the probability of message from gene a binding to ribosomes for translation.
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In this paper, a new simulation method for approximating the probability of failure and the most probable point of failure is proposed.
As in [20], we present a variant of this RANSAC-based algorithm, which improves the random selection step by employing a weight function approximating the probability of each pair of features in P to be matched with one in Q.
To explain the varying memory pool diversities in these different scenarios, we approximated the probability of an individual to become re-infected with the same pathogen and its expected lifetime.
Fu (2013) extended the previous inference framework (Gao et al. 2011) with a new method for approximating the probability of a mutation pattern and a refined coalescent algorithm for simulating sample genealogies which are necessary for deriving coefficients used in the analysis.
To explore the distribution of SUVmean and SUVmax a kernel density plot was used, which approximates the probability density of the variable; Kernel density plots have the advantage of being independent of the choice of origin, unlike histograms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com