Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-ofwords 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.
Also, the phylogenetic Indian buffet process (pIBP) REF was created as a non-exchangeable, nonparametric prior for latent feature models, where the dependencies between objects were expressed as tree structures.
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
ArXiv
ArXiv
Computer Science Mathematics