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  Probabilistic latent variable models for distinguishing between cause and effect

Mooij, J. M., Stegle, O., Janzing, D., Zhang, K., & Schölkopf, B. (2011). Probabilistic latent variable models for distinguishing between cause and effect. In J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 23 (pp. 1687-1695). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/2010/hash/c850371fda6892fbfd1c5a5b457e5777-Abstract.html.

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 Creators:
Mooij, J. M.1, Author           
Stegle, O.2, Author           
Janzing, D.1, Author           
Zhang, K.1, Author           
Schölkopf, B.1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497664              

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Free keywords: MPI für Intelligente Systeme; Abt. Schölkopf; Forschungsgruppe Borgwardt
 Abstract: -

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Language(s): eng - English
 Dates: 20102011-06
 Publication Status: Issued
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: 24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
Place of Event: Vancouver, BC
Start-/End Date: 2010-12-06 - 2010-12-09

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Title: Advances in Neural Information Processing Systems 23
  Subtitle : 24th Annual Conference on Neural Information Processing Systems 2010
Source Genre: Proceedings
 Creator(s):
Lafferty, J.1, Editor
Williams, C.1, Editor
Shawe-Taylor, J.1, Editor
Zemel, R.1, Editor
Culotta, A.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Red Hook, NY : Curran Associates, Inc.
Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: 1687 - 1695 Identifier: ISBN: 978-1-61782-380-0
URI: https://papers.nips.cc/paper/2010