Sentence examples for of statistical predictions from inspiring English sources

Limitations of statistical predictions are briefly discussed.

In this way the kriging variance is a useful pre-survey measure of the quality of statistical predictions, which can be used to design sampling schemes to achieve target quality requirements at minimal cost.

The validity of this type of approach has been verified both by computer simulations [5], [6] and by a growing number of cases, including recent reports of experimental verification of statistical predictions (for review see [7]).

The two isogenic stocks we used as parental lines, Canton-S and w, differ by 93,538 SNPs and InDel polymorphisms on the 22.4-Mb X chromosome, allowing us a to detect virtually all CO events and, on the basis of statistical predictions of the SNP distribution, approximately 40% of all GC events assuming an average conversion tract length of 476 bp.

Facing a team that was that hot, it is a testament to Missouri's talent that they managed to keep the game so close.For virtually opposite reasons, both games confirmed the value and accuracy of statistical prediction methods in college basketball.

Firstly, we review the fundamentals of statistical prediction.

This approach requires a development of statistical prediction method which accounts not only for the present condition but also for the series of past data.

Then, we will showcase three example workflows and their use at Genentech: (1) the creation of "project reports" visualized with Vortex [21], (2) the creation of statistical prediction models using the Random Forest machine learning method [22, 23], and (3) the implementation of a method to estimate the strain of 3D conformations of small molecules.

Moreover, this two-step procedure is a rather general idea that could be applied to all kinds of statistical prediction problems to find parameter consistent and model selection consistent estimates in high dimensions.

We compared these to existing validated instruments for the assessment of risk of violence (HCR-20) and self-harm (S-RAMM) and examined whether they accounted for any element of statistical prediction over and above an existing 'gold standard' instrument for the assessment of risk of violence, the HCR-20.

In particular, feed-forward neural networks (FFNNs), currently recognized as state-of-the-art approach for statistical prediction of air quality, are compared with two alternative approaches derived from machine learning: pruned neural networks (PNNs) and lazy learning (LL).