Sentence examples for computing prediction from inspiring English sources

We furthermore objectively and quantitatively assessed the applicability domains of the models by computing prediction confidence and domain extrapolation for predicting unknown chemicals with an extensive cross-validation.

In our experiments on a collection of several NIR data sets, we evaluate several existing methods of computing prediction intervals for partial least-squares (PLS) regression.

Although wind speed is difficult to forecast by single predictor, composite forecast model can statistically produce an optimal forecast by computing prediction results from a number of different methods.

For computing prediction intervals associated with different values of EffectTTP, an intercept term was included so that perfect prediction at the no effect point was not assumed.

Furthermore, we observe empirically that this approximation achieves reasonably accurate estimates of lower-order moments (e.g., posterior mean and variance) when the true posterior is replaced by the variational distributions (e.g., when computing prediction error on held-out entries of the genotype matrix).

Readers were invited to make predictions and collaboratively edit this timeline, which was divided into three sections: a sampling of past advances in computing, predictions that readers could push forward or pull backward in time with the click of a button (but not, of course, into the past), and a form for making and voting on predictions.

The fact that this is exactly what we measured in our experiment provides support for the notion that the pMNS is part of forward/generative neural model [14], [16], [23], [25] that could play a key role in joint actions not only by transforming observed and executed actions in a common code but also by computing predictions that can overcome otherwise inevitable neural delays.

Then, the calibrated models were tested by computing predictions for cross-validation data sets.

This ANN analysis supports the simultaneous training of thousands of network architectures, computing predictions from each network architecture to pinpoint the single best network.

Actual sensory feedback is used to compute prediction errors for model evaluation and update.

The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs.