no code implementations • 22 Jun 2019 • Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori, Hiroshi Tokieda
One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation.
no code implementations • 21 Sep 2018 • Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.
no code implementations • ICML 2018 • Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood.
no code implementations • 13 Jul 2016 • Keisuke Yamazaki
The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity.
no code implementations • 5 Oct 2015 • Keisuke Yamazaki
A previous study proposed a method that can be used when the range of the latent variable is redundant compared with the model generating data.
no code implementations • 25 Aug 2014 • Keisuke Yamazaki
In a previous study, we determined the accuracy of a Bayes estimation for the joint probability of the latent variables in a dataset, and we proved that the Bayes method is asymptotically more accurate than the maximum-likelihood method.
no code implementations • 9 Aug 2013 • Keisuke Yamazaki
Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones.
no code implementations • 15 May 2012 • Keisuke Yamazaki
In the singular case, on the other hand, the models are not identifiable and the Fisher matrix is not positive definite.
no code implementations • 10 Apr 2012 • Keisuke Yamazaki
Hierarchical statistical models are widely employed in information science and data engineering.