1. Introduction
Here we consider mathematical model of a measurement (the measurement model) of a single scalar quantity, say , which can be expressed by a functional relationship \begin{equation*} \text{Y}=f(X), \tag{1} \end{equation*} where is a scalar output quantity of interest (associated with the true value of the measurand) and represents the input quantities . Each is regarded as a random variable (RV) with possible values and is a RV with possible values . The joint probability density function (PDF) for is denoted here by and cumulative distribution function (CDF) is denoted by , where is a vector variable describing the possible values of the vector quantity PDF for is denoted by and the CDF by . Marginal distribution functions (PDFs/CDFs) for are denoted by and , respectively. Frequently, it is adequate to assume that the functional relationship is linear in , i.e.\begin{equation*} \text{Y}=c_{1}X_{1}+\cdots+c_{n}X_{n}, \tag{2} \end{equation*} for some known constants (the sensitivity coefficients), and moreover, that the input quantities are mutually independent RVs with known distributions.