  Nonparametric Bayesian models are often based on the assumption that the
objects being modeled are exchangeable. While appropriate in some applications
(e.g., bag-of-words models for documents), exchangeability is sometimes assumed
simply for computational reasons; non-exchangeable models might be a better
choice for applications based on subject matter. Drawing on ideas from
graphical models and phylogenetics, we describe a non-exchangeable prior for a
class of nonparametric latent feature models that is nearly as efficient
computationally as its exchangeable counterpart. Our model is applicable to the
general setting in which the dependencies between objects can be expressed
using a tree, where edge lengths indicate the strength of relationships. We
demonstrate an application to modeling probabilistic choice.
