  Given a sequence \xi_1, \xi_2,... of X-valued, exchangeable random elements,
let q(\xi^(n)) and p_m(\xi^(n)) stand for posterior and predictive
distribution, respectively, given \xi^(n) = (\xi_1,..., \xi_n). We provide an
upper bound for limsup b_n d_[[X]](q(\xi^(n)), \delta_\empiricn) and limsup b_n
d_[X^m](p_m(\xi^(n)), \empiricn^m), where \empiricn is the empirical measure,
b_n is a suitable sequence of positive numbers increasing to +\infty, d_[[X]]
and d_[X^m] denote distinguished weak probability distances on [[X]] and [X^m],
respectively, with the proviso that [S] denotes the space of all probability
measures on S. A characteristic feature of our work is that the aforesaid
bounds are established under the law of the \xi_n's, unlike the more common
literature on Bayesian consistency, where they are studied with respect to
product measures (p_0)^\infty, as p_0 varies among the admissible
determinations of a random probability measure.
