Probabilistic Framework for Dynamic Measurements for Markovian Models With a Uniform Prior for the Measurand

Abstract

A general probabilistic model of the measurement process is discussed, showing how it can address both static and dynamic measurements. Emphasis is placed on the latter, and two main kinds of dynamic measurements are considered: direct, where the goal is to monitor a phenomenon over time, and indirect, where an alternative representation is sought, such as, typically, the spectrum. It is shown that both cases can be treated in the same framework, through a proper interpretation of the variables involved. Then, direct measurements are investigated in greater detail, and the application of this approach is discussed, in the case of a Markovian model with a uniform prior for the measurand, and an illustrative numerical example is presented. These results may pave the way to a better harmonization of measurements, grounded on a sound scientific foundation.

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