### 抜粋

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering of variables in a linear acyclic model without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model.

元の言語 | 英語 |
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ページ | 506-513 |

ページ数 | 8 |

出版物ステータス | 出版済み - 12 1 2009 |

外部発表 | Yes |

イベント | 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 - Montreal, QC, カナダ 継続期間: 6 18 2009 → 6 21 2009 |

### 会議

会議 | 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 |
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国 | カナダ |

市 | Montreal, QC |

期間 | 6/18/09 → 6/21/09 |

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence
- Applied Mathematics

## フィンガープリント A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

## これを引用

*A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model*. 506-513. 論文発表場所 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009, Montreal, QC, カナダ.