  In the 1920's, the English philosopher W.E. Johnson introduced a
characterization of the symmetric Dirichlet prior distribution in terms of its
predictive distribution. This is typically referred to as Johnson's
"sufficientness" postulate, and it has been the subject of many contributions
in Bayesian statistics, leading to predictive characterization for
infinite-dimensional generalizations of the Dirichlet distribution, i.e.
species-sampling models. In this paper, we review "sufficientness" postulates
for species-sampling models, and then investigate analogous predictive
characterizations for the more general features-sampling models. In particular,
we present a "sufficientness" postulate for a class of features-sampling models
referred to as Scaled Processes (SPs), and then discuss analogous
characterizations in the general setup of features-sampling models.
