From entropic constraints to reinforced processes: a probabilistic origin of multiscale measures
We study multiscale measures, defined by a hierarchy of partial Gibbs equilibria, from variational and probabilistic perspectives. We first show that they are the unique solutions of a generalized maximum-entropy principle in which conditional entropies at different levels carry different weights. We then construct a hierarchical reinforced multinomial process proving a large-deviation principle for its empirical measure. The stochastic process leaves the typical empirical distribution unchanged but modifies the exponential cost of its fluctuations. The resulting rate function is a weighted sum of conditional Kullback-Leibler divergences, establishing a direct link with the entropic principle above. The relation between the reinforcement and Poisson-Dirichlet processes is discussed.