Sentence examples for probability of characterizing from inspiring English sources

Exact(1)

A reciprocal best hit where a given HLA aligns best to a given contig doubles the score for the identified HLA sequence: S H L A = ∑ C o n t i g = 1 n S c o r e C o n t i g = s i z e * d e p t h * % s e q u e n c e _ i d e n t i t y For any given contig, the probability of characterizing a single HLA allele by chance is equal to the inverse proportion of HLA sequences in the sequence database.

Similar(59)

The intercepts of the Poisson mean and of the probability of death characterize transition of the people who had the best cognitive performance at baseline and therefore can be used in studying the factors influencing such performance to better understanding the initial stages of impairment.

have led to the understanding that comprehensive plans to ensure public safety are the result of characterizing both the probability of an event and the severity of the outcome should the event occur.

TreeAge Pro 2011 software was used to build a static decision tree model, which is appropriate for the analysis of the probabilities of events characterized by limited change or recurrence over time, such as the probability that a newborn is hearing impaired or not [ 17- 19].

It begins by developing the network that plausibly represents the disease of concern, choose probabilities to characterize uncertainty, conditioning them on current information and knowledge, and infer accordingly.

These vectors are characterized by the correlation matrices:R e  D  D(b)  diag(b) (a diagonal matrix with vector b at its principal diagonal),, and + R n, respectively, where defines the averaging performed over the randomness of characterized by the probability density function p unknown to the observer, and superscript + stands for Hermitian conjugate (conjugate transpose).

However with a high probability it is known that the pathogenesis of characterized disorders is multifactorial.

(3) The occurrence probability of ESF is characterized by a continuous probability distribution as a function of the PRE magnitude.

We derive upper bounds on the codeword error probability of RRNS STBC and characterize its achievable diversity gain assuming maximum likelihood decoding (MLD).

In this section, we aim at using this result to elaborate a statistical model for G, i.e., a closed form expression of the probability law, characterized by the shadowing parameter σdB.

Philosophers concerned with the interpretation of probability are usually dealing with the following problem: Probability is characterized by a number of formal rules, the additivity of probabilities for disjoint sets of possibilities being the most central of these.

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