Sentence examples for variation predictions from inspiring English sources

A possible step towards improved secular variation predictions using core surface flows and ensemble Kalman filter techniques is explored by Beggan and Whaler.

The secular variation predictions of our parent models, from which the candidate models were derived, have been validated against independent ground observatory data.

Stock price variation predictions are at the core of many research issues, and neural networks (NNs) are widely applied and were proven to be more efficient than time series forecasting for stock price forecasting.

Those expressing objections never took any notice of the fact that in the study of sociolinguistic variation, predictions about linguistic features correlating with situational ones such as age, gender, geographical and/or social provenance, have typically been stated in probabilistic terms, and continue to do so even today.

The internal part of CHAOS-5 is time-dependent up to degree and order 20 and involves sixth-order splines with a 0.5-year spacing to provide secular variation information between 1999 and 2015 as well as secular variation predictions from 2015 till 2020.

The internal part of CHAOS-6 is time-dependent up to degree and order 20 and involves sixth-order splines with a 0.5-year knot spacing to provide secular variation information between 1997 and 2016 as well as secular variation predictions till 2020.

In this paper, we present a novel appearance variation prediction model which can be embedded into the existing generative appearance model based tracking framework.

The largest differences between the independent core models and the Swarm models will be due to the uncertainties in the secular variation prediction, so data from ground-based observatories will also be used to verify the Swarm models.

As component mode synthesis (CMS) is adopted in order-reduced modeling, we then utilize the improved response variation prediction on modal characteristics to update the CMS model to facilitate the efficient probabilistic analysis of any responses of concern.

The deleterious variation prediction here is considered to alter the structure of protein rather than the protein becoming non-functional.

Oishi et al. [ 80] assessed multiple layers of regulatory evidence, such as topology, conservation, TFBS prediction, and TF binding, for regulatory variation prediction.