13 citations · 17 across the 5 of their papers we have counts for
5 papers
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
Naoki Tanaka, Shohei Shimizu, Takashi Washio
A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data. A linea…
Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders
Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen +1
We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal…
Learning a Common Substructure of Multiple Graphical Gaussian Models
Satoshi Hara, Takashi Washio
Properties of data are frequently seen to vary depending on the sampled situations, which usually changes along a time evolution or owing to environmental effects. One way to analy…
Discovering causal structures in binary exclusive-or skew acyclic models
Takanori Inazumi, Takashi Washio, Shohei Shimizu +3
Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discove…
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu +1
Finding the structure of a graphical model has been received much attention in many fields. Recently, it is reported that the non-Gaussianity of data enables us to identify the str…