A non-parametric Bayesian method for inferring hidden causes

Frank Wood; Thomas L. Griffiths; Zoubin Ghahramani

A non-parametric Bayesian method for inferring hidden causes

Frank Wood, Thomas L. Griffiths, Zoubin Ghahramani

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Pages	536-543
Number of pages	8
State	Published - 2006
Externally published	Yes
Event	22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States Duration: Jul 13 2006 → Jul 16 2006

Publication series

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

Other

Other	22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Country/Territory	United States
City	Cambridge, MA
Period	7/13/06 → 7/16/06

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

@inproceedings{50e9c936fe1949549b66b220e7f2fa6b,

title = "A non-parametric Bayesian method for inferring hidden causes",

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.",

author = "Frank Wood and Griffiths, {Thomas L.} and Zoubin Ghahramani",

year = "2006",

language = "English (US)",

isbn = "0974903922",

series = "Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006",

pages = "536--543",

booktitle = "Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006",

note = "22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 ; Conference date: 13-07-2006 Through 16-07-2006",

}

Wood, F, Griffiths, TL & 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. Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006, pp. 536-543, 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006, Cambridge, MA, United States, 7/13/06.

A non-parametric Bayesian method for inferring hidden causes. / Wood, Frank; Griffiths, Thomas L.; Ghahramani, Zoubin.
Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006. 2006. p. 536-543 (Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Griffiths, Thomas L.

AU - Ghahramani, Zoubin

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:80053160972

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T3 - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

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EP - 543

BT - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

T2 - 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Y2 - 13 July 2006 through 16 July 2006

ER -

A non-parametric Bayesian method for inferring hidden causes

Abstract

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All Science Journal Classification (ASJC) codes

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