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Nonparametric Bayesian models are often based on the assumption that theobjects being modeled are exchangeable. While appropriate in some applications(e.g., bag-of-words models for documents), exchangeability is sometimes assumedsimply for computational reasons; non-exchangeable models might be a betterchoice for applications based on subject matter. Drawing on ideas fromgraphical models and phylogenetics, we describe a non-exchangeable prior for aclass of nonparametric latent feature models that is nearly as efficientcomputationally as its exchangeable counterpart. Our model is applicable to thegeneral setting in which the dependencies between objects can be expressedusing a tree, where edge lengths indicate the strength of relationships. Wedemonstrate an application to modeling probabilistic choice.

The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features