subrandom造句

例句与造句

1. Subrandom numbers can also be combined with search algorithms.
2. In more than one dimension, separate subrandom numbers are needed for each dimension.
3. Sequences of subrandom numbers can be generated from random numbers by imposing a negative correlation on those random numbers.
4. Subrandom numbers have an advantage over pure random numbers in that they cover the domain of interest quickly and evenly.
5. On the other hand, subrandom sets can have a significant lower discrepancy for a given number of points than subrandom sequences.
6. It's difficult to find subrandom in a sentence. 用subrandom造句挺难的
7. On the other hand, subrandom sets can have a significant lower discrepancy for a given number of points than subrandom sequences.
8. Subrandom numbers can also be used for providing starting points for deterministic algorithms that only work locally, such as Newton Raphson iteration.
9. With a search algorithm, subrandom numbers can be used to find the cumulative distribution of a statistical distribution, and all local minima and all solutions of deterministic functions.
10. A binary tree Quicksort-style algorithm ought to work exceptionally well because subrandom numbers flatten the tree far better than random numbers, and the flatter the tree the faster the sorting.
11. Because any distribution of random numbers can be mapped onto a uniform distribution, and subrandom numbers are mapped in the same way, this article only concerns generation of subrandom numbers on a multidimensional uniform distribution.
12. Because any distribution of random numbers can be mapped onto a uniform distribution, and subrandom numbers are mapped in the same way, this article only concerns generation of subrandom numbers on a multidimensional uniform distribution.
13. One way to do this is to start with a set of random numbers r _ i on [ 0, 0.5 ) and construct subrandom numbers s _ i which are uniform on [ 0, 1 ) using:
14. They have an advantage over purely deterministic methods in that deterministic methods only give high accuracy when the number of datapoints is pre-set whereas in using subrandom sequences the accuracy typically improves continually as more datapoints are added, with full reuse of the existing points.
15. Error in estimated kurtosis as a function of number of datapoints .'Additive subrandom'gives the maximum error when " c " = ( & radic; 5 & minus; 1 ) / 2 .'Random'gives the average error over six runs of random numbers, where the average is taken to reduce the magnitude of the wild fluctuations