Sentence examples for in case of sparse from inspiring English sources

In case of sparse networks (e.g., Northern Canada and Russia, Antarctica region and coastal zone in polar regions), the adjunction of the GLONASS-based measurements, due to the different constellation configuration as compare to the GPS one, allows to noticeably extend areas covered by the GNSS measurements and essentially increase a number of the available ionospheric piercing points.

Nevertheless, the unreliability of estimates in case of sparse data within age groups may remain a concern for both the traditional and the alternative method.

However, instead of referring to an energy expression, we rather look at the problem as a log odds score between the probability of observing a particular interaction between partner s with conformation c relative to some reference state: (2) S (c | s ) = − l n (p (c | s ) p (c ) ) In case of sparse data, p(c|s) cannot be expected to be saturated.

Because of the large-scale data generated for each completed methylation assay, techniques to select a subset of informative variables are needed [ 10– 12], particularly in cases of sparse data in which the vast majority of predictors are uninformative [ 13].

Genomedata is especially suited for whole-genome, dense datasets, so it has less of a comparative advantage in cases of sparse datasets with data at only a limited number of genomic positions.

While the intent of using simulated evolution in conjunction with simplified MRFs is to compensate for the removal of highly-interleaved beta-strand pairs required for computational feasibility, we find that simulated evolution can still improve full-fledged SMURF in cases of sparse training data.

While the primary intent of using simulated evolution in conjunction with simplified MRFs is to compensate for the removal of highly-interleaved beta-strand pairs required for computational feasibility, we surprisingly find that simulated evolution can still improve full-fledged SMURF in cases of sparse training data.

However, in case of a sparse training set, the most substantial probability would be for staying in the same state.

Note here that in case of a sparse graph the computational cost (as well as the communication one) of the basic protocol would be much lower.

In particular, in the case of sparse nodes, the performance of QMOR degrades very seriously.

We emphasise that deriving estimates of epidemiological parameters from multiple data sources depends heavily on the degree of accuracy, representativeness, and bias in those sources; in the case of sparse available data (such as the situation in Malaysia), these factors are even more crucial for producing valid estimates.