Bayesian approaches to learning problems have
many virtues, including their ability to make use of prior knowledge
and their ability to link related sources of information, but they also
have many vices, notably the strong parametric assumptions that are
often invoked in practical Bayesian modeling. Nonparametric Bayesian
methods offer a way to make use of the Bayesian calculus without the
parametric handcuffs. In this talk I describe several recent
explorations in nonparametric Bayesian modeling and inference,
including various versions of ``Chinese restaurant process priors''
that allow flexible structures to be learned and allow sharing of
statistical strength among sets of related structures. I discuss
applications to problems in bioinformatics and information retrieval.
Joint work with Yee Whye Teh and David Blei.