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Stochastic measurement procedure
・ Stochastic modelling (insurance)
・ Stochastic Models
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・ Stochastic neural network
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Stochastic measurement procedure : ウィキペディア英語版
Stochastic measurement procedure

A measurement is used to determine the actual value of a characteristic that is usually called measurand. A measurement is possible only if the measurand had been quantified prior to measurement by means of a suitable unit so that each value of the measurand is represented by a unique real number. For example, the characteristic "length" of a material object is quantified by the unit "meter", or the characteristic "(time) duration" of a development is quantified by the unit "second". Any measurement assumes a measurement process which is subject to randomness resulting in uncertainty about its indeterminate future outcome. Because of this uncertainty with respect to the future outcome of the measurement process, it is generally impossible to determine the true value of the measurand.
The related problems are addressed in the ISO Guide to the Expression of Uncertainty in Measurement〔Guide to expression of uncertainty in measurement, ().〕 (GUM) which was first published in 1993. However, since the GUM was published, complaints and critiques about it did not cease.〔Elart von Collani, A critical note on the Guide to the Expression of Uncertainty in Measurement (GUM), ''Economic Quality Control'', Vol. 23, 123−149, 2008.〕 The weaknesses of the GUM were one reason that stochastic measurement procedures〔Elart von Collani and Monica Dumitrescu, Complete Neyman measurement procedures, ''Metrika'', Vol. 54, 111−139, 2001.〕 were introduced in 2001. They are based on a rigorous introduction of the concepts of randomness and uncertainty
==Measurement procedure==

The measurand is given by a variable with fixed, i.e., determinate, but unknown value, and it is therefore called deterministic variable. A measurement itself is performed by a measurement device that defines a measurement process. In contrast to the measurand the measurement process is subject to randomness and its future outcome is therefore indeterminate. Consequently, the measurement process is represented by a variable ''X'' which is called a random variable.
The task is to conclude the unknown value of the measurand from the observed outcome of the measurement process. It is impossible to determine the true value of the measurand by means of the measurement process because of randomness. It is only possible to specify a set of values that includes the true value. Such a set constitutes the measurement result and it is called "correct" if it contains the true value of the measurand and wrong if not.
A measurement procedure is specified by the measurand given by the deterministic variable ''D'', a measurement device that defines the admitted measurement range denoted \mathfrak and the measurement process represented by the random variable ''X'', and finally the measurement function C_D^ which assigns to each observation \
with respect to the random variable ''X'' an measurement result C_D^(\).〔Elart von Collani, The Neyman theory − a scienfific measurement theory, in: V.P. Bulatov, I.G. Friedlaender (eds.): ''Basic Problems of Precision Theory'', Nauka, St. Petersburg, pp. 74−94, 2001 (in Russian).〕

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