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因果模型结合瞬时和滞后效应：基于非高斯的识别模型Causal Modelling Combining Instantaneous and Lagged Effects: an Identifiable Model Based on Non-Gaussianity |
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| 课程网址: | http://videolectures.net/icml08_hyvarinen_cmc/ |
| 主讲教师: | Aapo Hyvärinen |
| 开课单位: | 赫尔辛基大学 |
| 开课时间: | 2008-08-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian Bayesian networks poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model with autoregressive models. We show that such a non-Gaussian model is identifiable without prior knowledge of network structure, and we propose an estimation method shown to be consistent. This approach also points out how neglecting instantaneous effects can lead to completely wrong estimates of the autoregressive coefficients. |
| 关 键 词: | 高斯贝叶斯网络; 非高斯模型; 连续值变量的因果分析 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：张荧(课程编辑志愿者) |
| 阅读次数: | 31 |