This Bayesian framework naturally accommodates ancillary methods to address issues commonly seen in real computational and experimental data, including the presence of outliers, systematic errors and inaccurate error bars. Our novel approach optimally leverages specific heat and enthalpy **data** (as well as entropies and Gibbs free energies if available) to construct self-consistent thermodynamic models. One major advantage of this approach versus previous attempts is that it does not incur the errors associated with the conversion of all **data** to the same property basis (e.g. fitting and then differentiating enthalpy **data** to convert to specific heat). Furthermore, the framework can naturally incorporate the relative weighting of various datasets and provides significant insight into the relative importance of each dataset to the model. We demonstrate the strengths of the framework through the construction of a thermodynamic property model for Hafnium metal from a diverse collection of experimental measurements of heat capacity and enthalpy for the alpha, beta and liquid phases. Hafnium metal is a good candidate for this study as the available measurements exhibit the previously mentioned issues due to the difficulty of obtaining Hafnium samples without Zirconium content, the potential for oxygen contamination, and the extreme temperatures at which the beta and liquid phases are stable (Hafnium melts at ∼2500K and vaporizes at 4900K) [17].