Sentence examples for empirical features of from inspiring English sources

Two empirical features of this network are particularly relevant to our forecasting approach: edge innovation and transitivity.

In this paper, a novel framework for reduced order modeling in reservoir engineering is introduced, where tensor decompositions and representations of flow profiles are used to characterize empirical features of flow simulations.

However, I closely follow Arndt et al. ([2000]) and Lofgren et al. ([2013]) for the specification of many structural and empirical features of the Congolese economy, namely an explicit modeling of trade and transportation costs for marketed commodities and relatively detailed description of home production.

The third step derives confidence bounds for desired empirical features of the computed ranking network by accommodating heterogeneous measurement error.

In an attempt to interpret the empirical features of the PID distribution described above we propose a simple stochastic birth and death model of the proteome evolution.

This blend of empirical features predicates radical excursions of the dedicated models, and hence of the more general scientific paradigms, to allow consciousness and its subjective information processing capacities a proactive role in the establishment of objective reality, with all of the complications of specificity, causality, and reproducibility that entails.

These empirical features include the right-skewed distribution of collaboration sizes (Figure 3), the distribution of activities to engage in a collaboration (Figure 4) which are very stable across domains and over time, the pronounced community structure of the networks and the existence of a giant-connected component (Figure 7).

Thus, it is possible that the empirical features do not capture all the information about the determinants of protein stability.

This schematic provides examples of spinal cord circuitry that are consistent with its empirical features and could mediate proprioceptive predictions.

Multiple Empirical Kernel Learning (MEKL) explicitly maps samples into different empirical feature spaces in which the kernel features of the mapped samples can be directly provided.

These performance measures are slightly better than those of the empirical feature S3, a protein stability scale based on experimental data [ 17].