  Learning of a smooth but nonparametric probability density can be regularized
using methods of Quantum Field Theory. We implement a field theoretic prior
numerically, test its efficacy, and show that the data and the phase space
factors arising from the integration over the model space determine the free
parameter of the theory ("smoothness scale") self-consistently. This persists
even for distributions that are atypical in the prior and is a step towards a
model-independent theory for learning continuous distributions. Finally, we
point out that a wrong parameterization of a model family may sometimes be
advantageous for small data sets.
