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Discovering Cyclic Causal Models by Independent Components Analysis Gustavo Lacerda Machine Learning Department School of Computer Science
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Document Date: 2008-05-22 13:41:49

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City

New York / /

Company

Cambridge University Press / BT / Shimizu / John Wiley & Sons / /

Country

Finland / /

Currency

pence / /

Facility

Computer Science Carnegie Mellon University / stable SEM / Computer Science University of Helsinki Helsinki / Philosophy Carnegie Mellon University / /

IndustryTerm

depthfirst search / cycle product / correct algorithm / mainstream dynamical systems / depth-first search / cycle-product / nonrecursive systems / search paths / local thresholding algorithm / k-th best assignment algorithm / dynamical systems / non-local algorithm / /

Organization

Computer Science University of Helsinki Helsinki / Cambridge University / National Science Foundation / Independent Components Analysis Gustavo Lacerda Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh / Peter Spirtes Joseph Ramsey Department of Philosophy Carnegie Mellon University Pittsburgh / /

Person

Anupam Gupta / Cosma Shalizi / Gustavo Lacerda / Michael Dinitz / /

Position

Fisher / linear / non-gaussian acyclic model for causal discovery / /

Product

Pentax K-x Digital Camera / LiNG / /

ProvinceOrState

Pennsylvania / New York / /

PublishedMedium

Journal of Machine Learning Research / /

Technology

ICA algorithm / assignment algorithm / ICA algorithms / LiNG-D algorithm / LiNGAM algorithm / provably correct algorithm / non-local algorithm Local algorithms / artificial intelligence / The LiNG-D algorithm / non-local algorithm / Polynomial-Time Algorithm / local thresholding algorithm / Machine Learning / simulation / Constrained n-Rooks These algorithms / The LiNG discovery algorithms / LiNG discovery algorithms / local algorithm / LiNG discovery algorithm / 3.2 The LiNGAM discovery algorithm / /

URL

www.phil.cmu.edu/~tetrad / /

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