Sentence examples for using a decision rule from inspiring English sources

Supervised approaches are often done using a decision rule or expert knowledge.

A binary hypothesis test is then formulated using a decision rule, resulting in H 0 : d ˜ k < λ H 1 : d ˜ k ≥ λ. (17).

In summary, for children with suspected bacterial meningitis, we recommend using a decision rule as an aid to triage decisions and antibiotic prescribing, such as the BMS (more specific, but with a few false-negatives) or the Meningitest® (less specific, but no false-negative described to date).

Increasing the sample size reduced the cost-effectiveness of using a decision rule with the standard TAS design (sample size of 308).

Using a decision rule to biopsy those with a 20% or higher risk of cancer from the model would reduce the number of biopsies by nearly half.

In summary, for children with suspected bacterial meningitis, we recommend using a decision rule as an aid to triage decisions and antibiotic prescribing, such as the BMS (more specific, but with a few false-negatives) or the Meningitest ® (less specific, but no false-negative described to date).

The time-domain detection technique uses a decision rule that is derived from a time-domain correlation between the transmitted and received SCI, and as a consequence, eliminates the need for channel estimation at the receiver.

In addition to function Score P e), exSPA nder uses a decision rule Extend(P) that either chooses one of the extension edges to extend the path P or makes the decision to stop growing this path beyond the ending vertex of P. The procedure is iterated over all the paths until no path can be further extended.

The Bayesian model does not use a weighted scoring system, it uses a decision rule that enables the probability of morbidity in patients undergone CABG surgery to be assessed according to multivariate statistics of the predictor variables used for discrimination (12 variables were selected in our model).

When however the conditional posterior distribution of a subset of the parameters is not easy or even impossible to sample from directly, other MCMC algorithms can be used, where one samples from a similar proposal distribution and uses a decision rule to either accept or reject a sample so that the accepted values can be regarded drawings from the target distribution.

To choose the six NITs, we used a decision rule whereby we chose the best NIT (defined as the most cost-effective NIT at a cost-effectiveness threshold of £20,000) from within each category with a defined diagnostic cutoff (high or low) and the best NIT without any defined cut-off.