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  Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions

Sun, X., Janzing, D., & Schölkopf, B. (2007). Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), 441-446.

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資料種別: 会議論文

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 作成者:
Sun, X1, 著者           
Janzing, D2, 著者           
Schölkopf, B1, 著者           
Verleysen, M., 編集者
所属:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 要旨: We propose a method to evaluate the complexity of probability measures from data that is based on a reproducing kernel Hilbert space seminorm of the logarithm of conditional probability densities. The motivation is to provide a tool for a causal inference method which assumes that conditional probabilities for effects given their causes are typically simpler and smoother than vice-versa. We present experiments with toy data where the quantitative results are consistent with our intuitive understanding of complexity and smoothness. Also in some examples with real-world data the probability measure corresponding to the true causal direction turned out to be less complex than those of the reversed order.

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 日付: 2007-04
 出版の状態: 出版
 ページ: -
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 識別子(DOI, ISBNなど): URI: http://www.dice.ucl.ac.be/esann/proceedings/papers.php?ann=2007
BibTex参照ID: 4454
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イベント名: 15th European Symposium on Artificial Neural Networks
開催地: Brugge, Belgium
開始日・終了日: -

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出版物名: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
種別: 学術雑誌
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出版社, 出版地: Evere, Belgium : D-Side Publications
ページ: - 巻号: - 通巻号: - 開始・終了ページ: 441 - 446 識別子(ISBN, ISSN, DOIなど): -