The compilation of thermodynamic models for geophysical applications is such a tedious and complex process that it is generally impractical for researchers to refit parameters in existing models in light of new constraints. To mitigate this difficulty, we develop a Bayesian algorithm that permits the modification of a thermodynamic model to account for additional observational constraints. This algorithm can be applied to any thermodynamic dataset and can utilize a wide variety of experimental constraints. To demonstrate the applicability of the algorithm it is used to revise the Stixrude and Lithgow-Bertelloni (2011,

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