A non-parametric Bayesian method for inferring hidden causes

Frank Wood, Thomas L. Griffiths, Zoubin Ghahramani

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

52 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Pages536-543
Number of pages8
StatePublished - 2006
Externally publishedYes
Event22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States
Duration: Jul 13 2006Jul 16 2006

Publication series

NameProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Other

Other22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
CountryUnited States
CityCambridge, MA
Period7/13/067/16/06

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Wood, F., Griffiths, T. L., & Ghahramani, Z. (2006). A non-parametric Bayesian method for inferring hidden causes. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 (pp. 536-543). (Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006).