例句與造句

1. The first type is called stochastic prediction procedure, the second type stochastic measurement procedure.
2. In case of a stochastic prediction procedure it refers to the size of the predicted event.
3. A stochastic prediction procedure A _ X is derived in a way that it meets the following two requirements:
4. A stochastic prediction procedure aims at reducing the uncertainty about the future development of interest represented by the random variable " X ".
5. In addition, the method can easily incorporate the concepts of stochastic predictions, using the program evaluation and review technique ( PERT ) and event chain methodology.
6. It's difficult to find stochastic prediction in a sentence. 用stochastic prediction造句挺難的
7. This facility makes it also possible to use various generated rainfall sequences drawn randomly from a known rainfall probability distribution and to obtain a stochastic prediction of the resulting output parameters.
8. A stochastic prediction procedure is a mathematical function denoted A ^ { ( \ beta ) } _ X defined on sets representing the possible initial conditions and having images that are the predictions.
9. The characteristic issues of a stochastic prediction procedures shall be illustrated by a comparison with a prediction made and published by the University Corporation for Atmospheric Research ( UCAR ) which is described in as follows:
10. A stochastic prediction procedure is a function denoted A _ X which assigns to each level of knowledge ( = subset of the ignorance space ) a prediction, i . e ., a subset of the corresponding range of variability of $ X $.
11. The derivation of a stochastic prediction procedure is therefore based on a Bernoulli Space which represents a stochastic model of the change from past to future taking into account the characteristic human ignorance about the initial conditions and the inherent randomness of the evolution of universe.
12. This stochastic model is called Bernoulli space and enables the development of reliable and accurate stochastic prediction procedures given by the function A _ X ^ { ( \ beta ) } with the domain being the possible values of the measurand $ D $ where the reliability level \ beta is a lower bound for the probability that an obtained prediction will actually occur.