But he soon outgrew this phase to develop a prodigious craft which exploits character as much as precision-engineered situations.
The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.
In both situations, precision must be good over different conditions (reproducibility) and over the requested measuring interval (stability, linearity).
Compared with the existing algorithms, the proposed algorithm in this paper obtains high precision under the situation of both slow and fast phase fluctuation and better robustness to noise.
Compared with the CED method, the algorithm proposed in this paper has the following advantages: 1. this algorithm has higher estimation precision under the situation of both slow phase fluctuation and fast phase fluctuation than the CED method; 2. this algorithm is more robust to noise.
In each situation, precision was measured at 10% recall intervals and was also measured for the top 10, 20, 30, 40 and 50 results.
As in other situations where precision is critical, machines can be of help.
However, and in many situations, maximum precision is attained when sampling consists of replicates of specific experimental points.
Additionally, in some situations, greater precision of a population genetic parameter estimate can be achieved by sampling both shared polymorphisms and fixed differences between two populations, which can only be observed by sampling loci that have different genealogies [e.g. 13].
This is of utmost importance in subjects requiring higher than routine accuracy and precision as, e.g., in situations of live kidney donor evaluations [ 35].
