Sentence examples for in different predictions from inspiring English sources

The two LES models treat the chemistry differently, which results in different predictions of the flame dynamic behavior and statistics.

where we compute E [ Z ] ≈ 1 | ω | ∑ l ∈ ω k z l, E [ Y ] ≈ 1 | ω | ∑ l ∈ ω k y l, E [ Z Y ] ≈ 1 | ω | ∑ l ∈ ω k z l y l, E [ Z 2 ] ≈ 1 | ω | ∑ l ∈ ω k z l 2. Note that the use of different ω k results in different predictions of these coefficients.

When the model has many estimated parameters and the corresponding data is a few, the over-fitting problem might lead to some unwarranted conclusions because the parameter set in the model might be a special domain of possible parameter sets which can similarly fit the data well, but result in different predictions.

Distributions of the same shape for different epidemiological traits often resulted in different predictions for disease risk and severity.

Since different choices of machine learning algorithms resulted in different predictions of performance, we conducted several comprehensive experiments to determine the optimal combinations of the algorithms.

Furthermore, CUs with different depths may be coded in different prediction modes, different partitioning modes, and different transform sizes.

Although previous models have been proven to be capable of predicting crash likelihood in order to proactively improve traffic safety on freeways, various modeling techniques result in different prediction accuracies.

These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.

In summary, at specific times when working memory is needed, the intrinsic recurrent neural cirtuits of the prefrontal cortex (Arnsten et al., 2012) may selectively and strategically exploit (differentially or in concert) its rich array of excitatory and inhibitory efferent connections to regulate firing rates in different prediction areas of the brain.

Although the two approaches for identifying TE-derived piRNAs result in considerably different predictions in respect to the percentage of piRNAs derived from TEs in An. gambiae, they still agree on the families of TEs with the highest abundance of mapped piRNAs (Fig.  3a d).

Where DAMs (and other mathematical models) contain parameters which are poorly understood and therefore particularly uncertain, for example, transmission probabilities, then different models of the same outcome can result in very different predictions, as was the case with predicting the population impact of chlamydia screening [ 9, 21- 23].