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Assuming that households did not become infested between the 2 treatments, we jointly estimated c, s, and n I/II by modeling the observed infestation as a system of 3 equations (observed infested twice, infested only treatment I, and infested only during treatment II) (Table 3) that we solved analytically.
Previous studies suggested that a single virus established productive infection, but these conclusions were tempered because of limited sampling; now, we have greatly increased our confidence in this observation through modeling the observed earliest sample diversity based on vastly more extensive sampling.
This was possible by modeling the observed malaria deaths in a Poisson regression using the log of all known observed deaths as an offset.
It is found that the KGD performed as well as the EEGD in modeling the observed numbers of consumers.
Let us begin with modeling the observed apparent radiance at window Ω i with pixel location i as a three-dimensional random variable X i, X i = x | x ∈ Ω i. (5).
By modeling the observed displacement as the amplitude term multiplied by the propagation phase shift term, we can separate a term related to the geometrical spreading and radiation pattern from the slowness vector.
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However, we show how a simple simulation algorithm is used both to calibrate the system and to model the observed response to a high level of accuracy.
Approximating answers to these questions necessitates being able to model the observed bin spectrum.
For this reason, the selection of the parametric distribution to model the observed data should be done carefully.
Generally, log-linear regressions are used to model the observed data, assuming a multinomial or (equivalently) Poisson likelihood (29).
However, there appears to be exceptions, like Pyrococcus furiosus, for which the dicodon bias model failed to model the observed OSC frequency (Table 1).
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CEO of Professional Science Editing for Scientists @ prosciediting.com