  This paper explores large sample properties of the two-parameter
$(\alpha,\theta)$ Poisson--Dirichlet Process in two contexts. In a Bayesian
context of estimating an unknown probability measure, viewing this process as a
natural extension of the Dirichlet process, we explore the consistency and weak
convergence of the the two-parameter Poisson--Dirichlet posterior process. We
also establish the weak convergence of properly centered two-parameter
Poisson--Dirichlet processes for large $\theta+n\alpha.$ This latter result
complements large $\theta$ results for the Dirichlet process and
Poisson--Dirichlet sequences, and complements a recent result on large
deviation principles for the two-parameter Poisson--Dirichlet process. A
crucial component of our results is the use of distributional identities that
may be useful in other contexts.
