Sentence examples for models and unreliable from inspiring English sources

To avoid unstable models and unreliable assessment of model performance [ 12] instead of building the logistic models from a subset of the dataset and validating the model on the remainder, the models were developed from the entire dataset.

Single server non-Markovian model with removable and unreliable server under (p, N) -policy was discussed by Wang and Huang (2009).

Here are the concerns whether the following properties of the SD-model are good or bad for the purpose of genomes ranking: The SD-model can use subjective and unreliable judgments as an input and produce a misleading output with realistically-looking confidence levels.

The centrifuges being installed at Fordow, American officials noted, are of the old and unreliable model that Iran obtained from Pakistan, and they do not include any newer, more efficient models that the Iranians have claimed, for years, that they would move to.

A very rough and unreliable model of the Analytical Engine was the only version of the design that Babbage was able to complete.

In this direction, a few researchers have contributed by developing Markovian models with unreliable and vacation servers.

One consequence is that models and their predictions remain unreliable (Trenberth 2011; Stevens and Bony 2013; Shepherd 2014; Marotzke et al. 2017).

Open image in new window Fig. 13 Comparisons of responses based on Set A and Set B in Case 3. In this paper, a wind farm modeling and parameter identification approach based on measured data of PMUs is proposed to solve the problem of unreliable models and the inaccurate parameters.

A brief exploration of interactions between ventilation type and each sanitation variable revealed a significant positive interaction between vertical ventilation shafts and the use of bacterial disinfectant and/or fumigation, although, we did not include this interaction in the comprehensive model due to highly inflated and unreliable parameter estimates.

Current statistical methods and machine learning algorithms used for DEG selection only focus on the changes of the gene expression levels between the two groups of clinical samples instead of the causes behind these changes and subsequently result in a number of false positive genes unrelated to the phenotypic differences involved in the DEG list and the predictive models becoming unreliable.

If melt ponds are reported as clear water then data is skewed and unreliable as are the model outputs upon which alarmists cling to for life.