کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410080 | 679119 | 2012 | 4 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Joint estimation of linear non-Gaussian acyclic models
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different conditions, that is, multiple datasets are obtained rather than a single dataset. In this paper, we present a new method to jointly estimate multiple LiNGAMs under the assumption that the models share a causal ordering but may have different connection strengths and differently distributed variables. In simulations, the new method estimates the models more accurately than estimating them separately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 81, 1 April 2012, Pages 104–107
Journal: Neurocomputing - Volume 81, 1 April 2012, Pages 104–107
نویسندگان
Shohei Shimizu,