To tune the CSP viscosity model, we use a Bayesian approach to take into account uncertainties on experimental data. With tuning parameters calibrated through this approach, uncertainties on the CSP predicted viscosities can be estimated and we can assess the model limitations based on violations of credible regions. In contrast with classical deterministic fitting (e.g., Least-Squares regressions), the calibrated parameters are given as probability distributions, providing insights about the region of occurrence, the range of values and etc. Moreover, when tuning parameters' distributions are obtained by the calibration, an uncertainty propagation can be performed for predictive scenarios. Such additional information is not provided by deterministic fitting methods. As far as we know at the moment this manuscript is written, the aforementioned Bayesian analysis is unreported in the literature for CSP viscosity estimates.