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An optimal retrieval system should maximize this probability, which is equivalent to maximizing the posterior, that is, the relevant object is retrieved from the maximum a posteriori criterion, that is, (4).
We then maximize this probability with respect to μ, σ, and ρ.
We numerically maximize this probability over ρ C to obtain the likelihood of the coding model p C. We then evaluate the likelihood of the non-coding model p N in the same way, using Q N and an independent scale factor ρ N, and report the log-likelihood ratio as the result.
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An optimal solution maximizes this probability.
We seek the value t maximizing this probability, or equivalently, the log-likelihood function, Note that is a sum of log-probabilities log, weighted by their observed counts N ij.
We will identify the optimal local alignment for the observed sequence by maximizing this joint probability.
This should maximize the probability for ACR + patients to be correctly classified as ACR + patients (i.e., the sensitivity) while limiting the probability for ACR– patients to be incorrectly classified.
This approach would maximize the probability of high impact assignments within the shorter time frame.
This work aims to maximize the probability of successful decoding through proper rate allocation amongst video layers of different servers.
The rationale for this methodological revision was to maximize the probability of measuring higher numbers of CTCs at least in the untreated cohort.
This information is used here to maximize the probability of correct choice within available options for each estimation step.
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CEO of Professional Science Editing for Scientists @ prosciediting.com