discrete and continuous variables: the definition of discrete and continuous variables in terms of statistics is related to the probability density function. For a continuous variable, the cumulative probability density function is steady. For a discrete variable, the cumulative probability density function is unsteady, e.g. due to sudden offsets. Measured or observed data (here: comonomer content in bulk f i and polymer F i ) is commonly assumed to be continuous. probability density function (PDF): function of a continuous random variable in sample space. The function returns the probability or a relative likelihood of a variable possessing a certain value. In combination with a flat prior, the absolute likelihood of a variable taking on any specific value is zero. This is due to the allowance of the variable to take on any value (no values are preferred). flat or uninformative prior: a prior contains information regarding a certain variable that we definitely know of (boundary condition). If a prior is called flat, this means that within the boundary conditions, no specific values are preferred. In our case, the prior does contain the information that the reactivity ratios cannot take on negative values. However, the reactivity ratios are allowed to possess all values above zero with the same possibility during the inference process. informative prior/posterior: an informative prior may be understood as an updated flat prior (which did previously not contain any helpful information). This is achieved by appliance of the Bayes-Theorem and the likelihood estimation to yield the posterior.