Sentence examples for multivariate approach that from inspiring English sources

Here, we propose a novel, alternative multivariate approach that overcomes these limitations — whole brain probabilistic ordinal regression using a Gaussian process framework.

Despite the fact that most of the previous studies gave contrasting results, most were also able to distinguish wild and farmed fish with great accuracy through a multivariate approach that accounted for a wide range of elements, indicating that the trace elemental profile might be more appropriate than the presence or absence of a specific quantity of an element (Arechavala-Lopez et al. 2013a).

We analyzed our data with partial least squares [28], [38], [39], a multivariate approach that robustly identifies group-level spatiotemporal activity patterns correlated to neuronal activity (seed-PLS).

The sample size did not allow a multivariate approach that takes into account the potential confounding factors.

We investigated the robustness of this behavior to simultaneous perturbations in the network parameters using a novel multivariate approach that integrates global sensitivity analysis with decision-tree methods.

Bansal [ 26] describes a multivariate approach that involves fitting the diffraction pattern as a linear combination of model patterns representing crystalline and amorphous standards.

Existing multivariate approaches that concern the wind directionality effect are first reviewed.

This trait is the basis of leaf-margin analysis (NT-MAT relation) and a key component of all multivariate approaches that estimate paleotemperature [8], [10] [14], [71].

The last chapter is somewhat confusing, offering various multivariate approaches that could potentially be applied to ecosystems biomonitoring studies.

Specifically, we considered both univariate approaches that evaluate the relevance of each single gene independently from the others and multivariate approaches that take into account interdependencies among genes.

In particular, we considered both univariate approaches, where each feature is ranked individually, and multivariate approaches that take into account feature dependencies.