We consider the problem of learning stochastic tree languages, i.e. probability distributions over a set of trees T (F), from a sample of trees independently drawn according to an unknown target P . We consider the case where the target is a rational stochastic tree language, i.e. it can be computed by a rational tree series or, equivalently, by a multiplicity tree automaton. In this paper, we ...