Sentence examples for difficulty of estimation from inspiring English sources

To overcome the difficulty of estimation caused by the random delays, the single random delayed measurement system is firstly rewritten as the multiplicative noise constant-delay system.

37 We have compensated for the difficulty of estimation of sample size in this field by leveraging our capacity for scalable, low-cost recruitment.

The big error range using a FNM tool is a potential risk because of difficulties on estimation of system resistances and airflow distribution.

Second, because of the difficulties of estimation, most accretions to wealth are ordinarily not included in an individual's taxable income until they are "realized"—that is, converted into cash or some easily valued form.

a critical examination of the order of magnitude and of the difficulties of estimation of the classical heat dissipation in the cell leads to the conclusion that the excess enthalpy claimed by the authors cannot be significantly proved.

The mutual information criterion may be used in that context, but it suffers from the difficulty of its estimation through a finite set of samples.

Fortunately we can overcome the difficulty of parameter estimation by a process called non-dimensionalisation [58].

Some authors discourage the use of the gamma distribution for model fitting due to the difficulty of parameter estimation [ 33].

Given the difficulty of this estimation problem, I anticipate that full-likelihood methods that can leverage all of the information present in the sequence data will become increasingly important for robustly estimating shared evolutionary history across taxa [ 45].

Further studies to define a commercially available urine dipstick equitable to the UPCR for detecting urinary protein in a patient with dengue with good sensitivity and specificity would reduce the difficulty of UPCR estimation.

The difficulties of parameter estimation problems in systems biology do not only depend on the number of parameters, but also on the structure (flexibility and nonlinearity) of the dynamic model, and the amount of information provided by the (usually scarce and noisy) available data.