In metrological practice, indirect measurements, where the measured value is calculated from several directly measured input quantities are commonly performed [

1,

2]. Thereby, measurements of multiparametric (multidimensional) quantities, including measurements carried out using multiple sensors, have become one of the most important problems of metrology [

3]. The most difficult task in this procedure is the propagation of the information, in particular the measurement uncertainty, from input quantities

$\mathit{X}={\left({X}_{1},{X}_{2},\dots ,\text{}{X}_{N}\right)}^{T}$ which information is written in the form of a probability density function (PDF), via a mathematical model of the measurement

$\mathit{Y}=f\left(\mathit{X}\right)$ which describes the relationship between

$\mathit{X}$ and

$\mathit{Y}$, in the PDF of one or more output quantities

$\mathit{Y}={\left({Y}_{1},{Y}_{2},\dots ,\text{}{Y}_{N}\right)}^{T}$ [

4]. In this case,

$\mathit{\xi}={\left({\xi}_{1},{\xi}_{2},\dots ,\text{}{\xi}_{N}\right)}^{T}$ represent the possible values of

$\mathit{X}$ and

$\mathit{\eta}={\left({\eta}_{1},{\eta}_{2},\dots ,\text{}{\eta}_{N}\right)}^{T}$ represent the possible values of

$\mathit{Y}$ [

5]. The traditional practice of solving such problems is within the internationally accepted document, Guide to the Expression of Uncertainty in Measurement (GUM) [

6,

7]. The main task of this method is the propagation of standard uncertainties of input quantities, via a mathematical model of the measurement to the standard uncertainties of the output quantities. Depending on the desired coverage level, an expanded uncertainty usually with 95% confidence level, is used. This procedure is widely accepted due to its simplicity and in most cases, it satisfies. However, its implementation requires a number of assumptions such as: Input quantities are mutually independent, the mathematical model of the measurement is linear, the input values have a normal distribution that after propagation results in a normal distribution of the output quantities and a number of other assumptions that are widely described in the literature [

1,

4,

8]. Therefore, it follows that the GUM procedure has some disadvantages and is not satisfactory in all cases. Such examples are described in [

4,

8,

9] as well as in Supplement 1 [

10] on the main GUM document [

6]. In particular, Supplement 1 recommends the use of the Monte Carlo method (MCM) for the calculation of measurement uncertainty which has a number of advantages over the traditional GUM method [

11]. The main difference from the GUM is in fact that MCM perform numerical random sampling from the PDF of input quantities and propagate them through a mathematical model of the measurement that results in a numerical approximation of the PDF of output quantities [

12,

13,

14]. In contrast to the GUM procedure, which propagates only information about the standard uncertainties (Law of Propagation of Uncertainties), MCM carries much richer information from the PDF of input quantities

${g}_{{X}_{i}}\left({\xi}_{i}\right)$ in the form of a PDF’s output quantities

${g}_{Y}\left(\eta \right)$ (Law of Propagation of Distributions). After that, it is easy to calculate the desired parameters of output quantities, e.g., the mean, the standard deviation, uncertainty limits with the desired confidence level. MCM is performed with a predetermined number of iterations M [

15]. It is generally assumed that

M = 10

^{6} iterations satisfy for the 95% confidence level [

8,

10]. More details about the implementation of the MCM method could be found in the authors’ previous work [

16,

17,

18].