Uncertainty in Artificial Intelligence
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A Non-Parametric Bayesian Method for Inferring Hidden Causes
Frank Wood, Thomas Griffiths, Zoubin Ghahramani
Abstract:
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables. This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures. We evaluate the performance of both approaches in discovering hidden causes in simulated data, and use our non-parametric approach to discover hidden causes in a real medical dataset.
Keywords:
Pages: 536-543
PS Link:
PDF Link: /papers/06/p536-wood.pdf
BibTex:
@INPROCEEDINGS{Wood06,
AUTHOR = "Frank Wood and Thomas Griffiths and Zoubin Ghahramani",
TITLE = "A Non-Parametric Bayesian Method for Inferring Hidden Causes",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "536--543"
}


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