Made-to-measure methods such as the parallel code nmagic are powerful tools to build galaxy models reproducing observational data. They work by adapting the particle weights in an N-body system until the target observables are well matched. Here we introduce a moving prior entropy regularization (MPR) method for such particle models. It is based on determining from the particles a distribution of priors in phase space, which are updated in parallel with the weight adaptation. This method allows one to construct smooth models from noisy data without erasing global phase-space gradients. We first apply MPR to a spherical system for which the distribution function can in theory be uniquely recovered from idealized data. We show that nmagic with MPR indeed converges to the true solution with very good accuracy, independent of the initial particle model. Compared to the standard weight entropy regularization, biases in the anisotropy structure are removed and local fluctuations in the intrinsic distribution function are reduced. We then investigate how the uncertainties in the inferred dynamical structure increase with less complete and noisier kinematic data, and how the dependence on the initial particle model also increases. Finally, we apply the MPR technique to the two intermediate-luminosity elliptical galaxies NGC 4697 and NGC 3379, obtaining smoother dynamical models in luminous and dark matter potentials.