To perform the Bayesian calibration, instead of initial guesses to input parameters, prior distributions (P prior (X)) are required as initial hypothesis (e.g., input parameters are described by uniform distributions within given ranges). These distributions are estimates that must be the most informative as possible to have outcomes closer to observations . The better is the initial hypothesis, the better will be the posterior distribution. Another required information beforehand is the likelihood function (P likelihood (y|X)). Here we assume that observable data follow a normal distribution with mean value (µ) equal to the simulation result (CSP viscosity estimate) with a standard deviation σ. Thus, we have that: