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Results of the present study are based on transition probabilities drawn from real-life data and are applicable to the setting from which the results were derived.
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The prior interaction probabilities are drawn from two truncated normal distributions, x ± ~ N ± where N+ is the probability distribution for true interactions and N- is the probability distribution for false interactions.
As delineated in Table 1, transition probabilities are drawn from the best available estimates from the literature [ 16- 19].
Transition state probabilities were drawn from the historical cohort data and were unidirectional; patients in the deceased and lost to follow-up terminal states could not transition back to the alive and active in care state (a separate sensitivity analysis modeled patients classified as lost at 6 months who subsequently returned to the program).
It is based on a representative probability sample, drawn from the population register.
Data collected from such dynamic contact networks has been used to simulate the spread of a pathogen through the recorded network, using both the raw sample data [ 15, 23, 24], and aggregated versions where infection probability is drawn from an empirical rather than functional distribution [ 9].
The Behavioral Risk Factor Surveillance System data were obtained from an independent household probability sample drawn from the noninstitutionalized U.S. adult population (aged ≥18 years) to assess the prevalence of key behavioral risk factors and chronic disease conditions in all U.S. states and territories annually (5, 6).
The qualities of the TF target gene prediction results are evaluated by the ROC curves and the histograms of the estimated binding probabilities, which are drawn from the probabilities over all the TFs and the sequences being analyzed.
The probabilities of being drawn from each of these distributions result from the genetic history of the population.
First, if cells connect randomly with low probability to others drawn from a large population, then the probability of a cell having k connections would be expected to follow a Poisson distribution:, where the single parameter λ is the mean number of connections per neuron.
To reduce the probability that conclusions drawn from numerical results are purely coincidental, a large number of cases has been studied.
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CEO of Professional Science Editing for Scientists @ prosciediting.com