Sentence examples for flexible assumption from inspiring English sources

In this model we introduce the weaker and more flexible assumption that the technical marginal rate of substitution between skilled (resp. educated) labor and unskilled (resp. non-educated) labor is decreasing in the quality to be produced.

It is possible that unlikely detection configurations are allowed within this approach, but the absence of evidence to support a more restrictive parameterization led us to opt for the more flexible assumption.

The work presented in this paper, based on the regularity for solution of (1.1), investigates necessary and sufficient conditions for the approximate controllability for (1.1) with the strict range condition on B even though the system (1.1) contains unbounded principal operators and the convolution nonlinear term, which is a more flexible necessary assumption than the one in [2].

With these flexible modeling assumptions, a diverse set of scenarios and data properties can be accommodated.

Under flexible distributional assumptions, the adjusted quasi-maximum likelihood (adqml) estimator for mixed regressive, spatial autoregressive model is studied in this paper.

The artificial neural networks (ANN) approach is proposed for training-predicting the database in this paper since it is a more flexible and assumption-free methodology.

Because the majority of pavement construction done in Rhode Island involves flexible overlays, assumptions involving mixture densification due to traffic, distress severity levels, and aging rates were made to enable the proposed modeling of an overlay as two newly constructed layers in order to overcome the limitations in the QRSS software.

In addition to identifying desired patterns (e.g., NFR patterns) and capturing their quantitative features, our algorithm also enjoys more flexible model assumptions and higher efficiency compared to regular HMM.

Unlike classical modelling techniques, the stochastic gradient boosting algorithm utilizes very flexible structural assumptions and is capable of accommodating multiple sets of genes associated with different classification outcomes in a true multivariate fashion.

Using very flexible modeling assumptions, our results did not show any rationale for change of PM2.5 effect size over the whole study period, as indicated by the adjusted survival curves and the lack of a clear interaction of PM2.5 with the four study periods.

Further, our model has flexible data distribution assumption, enabling a unified interface for segmenting data generated from different experimental platforms.