  A widely used method to create a continuous representation of a discrete
data-set is regression analysis. When the regression model is not based on a
mathematical description of the physics underlying the data, heuristic
techniques play a crucial role and the model choice can have a significant
impact on the result. In this paper, the problem of identifying the most
appropriate model is formulated and solved in terms of Bayesian selection.
Besides, probability calculus is the best way to choose among different
alternatives. The results obtained are applied to the case of both univariate
and bivariate polynomials used as trial solutions of systems of thermodynamic
partial differential equations.
