Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100 1,000 times faster.
Although not as accurate as methods based on experimental procedures and usually requiring subsequent validation, this approach to computationally-infer regulatory networks can be useful to predict in-vivo functions of specific cell types [ 12].
A delay in the comparison measures may not be as accurate a method for comparing student perception and knowledge nor may asking students to make judgments about their knowledge over time.
While not as accurate as other methods, the trapezoidal methods are preferred because they are straightforward and are especially preferred when the estimate of area is the desired variable and the data between samples has similar structure (Yeh & Kwan, 1978).
As shown in Table 1, FastTree 2 was slightly more accurate than PhyML 3 with default settings (NNI search), and much more accurate than minimum-evolution or parsimony methods, but not as accurate as ML methods that use SPR moves.
Physical activity measurement in our study was based on self-reports via interviews, which is not as accurate a measurement method as objective methods, such as accelerometers [ 30].
Given qualifications of above studies, we believe that our method is at least as accurate as any method reported in the literature.
This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient.
The approach covers the whole range of heating rates that exist in combustion processes and it is as accurate as current methods.
