stochastic measurement procedure造句

A stochastic measurement procedure meets the following three requirements:
Any stochastic measurement procedure is based on a stochastic model of the measurement process.
The first type is called stochastic prediction procedure, the second type stochastic measurement procedure.
The weaknesses of the GUM were one reason that stochastic measurement procedures were introduced in 2001.
A stochastic measurement procedure aims at reducing the ignorance about the true but unknown value of the deterministic variable " D ".
It's difficult to find stochastic measurement procedure in a sentence. 用stochastic measurement procedure造句挺难的

The symbol \ beta is called reliability level of the stochastic measurement procedure and specifies a lower bound of the probability of obtaining a correct result when applying the measurement procedure.
Whenever the truth is given by a real number, say d _ 0, then a learning process is generally called measurement procedure or in case that it is based on a Bernoulli Space it is called stochastic measurement procedure.
A stochastic measurement procedure assigns to each outcome of the measurement process, i . e ., a subset of the range of variability of " X ", a measurement result, i . e ., a subset of the ignorance space.
In traditional metrology, " measurement precision " and " measurement accuracy " are distinguished, this somewhat confusing differentiation is not necessary for stochastic measurement procedures, since they distinguish between correct and wrong measurement results and meet a reliability specification given by the reliability level \ beta.
The measurement process has an indeterminate outcome which is represented by a random variable " X " and for deriving a suitable stochastic measurement procedure the uncertainty related to the measurement process must be described by a Bernoulli Space \ mathcal { B } _ { X, D }.
The accuracy of a stochastic measurement procedure is defined by the average size of the measurement results, i . e ., by the average size of the sets C _ D ^ { ( \ beta ) } ( \ { x \ } ) for all possible observations \ { x \ }.