Uncertainty in Artificial Intelligence
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The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
Kurt Miller, Thomas Griffiths, Michael Jordan
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-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.
Keywords: null
Pages: 403-410
PS Link:
PDF Link: /papers/08/p403-miller.pdf
BibTex:
@INPROCEEDINGS{Miller08,
AUTHOR = "Kurt Miller and Thomas Griffiths and Michael Jordan",
TITLE = "The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "403--410"
}


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