The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features

Kurt T. Miller, Thomas L. Griffiths, Michael I. Jordan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
Pages403-407
Number of pages5
StatePublished - 2008
Externally publishedYes
Event24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 - Helsinki, Finland
Duration: Jul 9 2008Jul 12 2008

Publication series

NameProceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Other

Other24th Conference on Uncertainty in Artificial Intelligence, UAI 2008
CountryFinland
CityHelsinki
Period7/9/087/12/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Miller, K. T., Griffiths, T. L., & Jordan, M. I. (2008). The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 (pp. 403-407). (Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008).