- 著者
- Shuichi Kawano Ibuki Hoshina Kaito Shimamura Sadanori Konishi
- 出版者
- 日本計算機統計学会
- 雑誌
- Journal of the Japanese Society of Computational Statistics (ISSN:09152350)
- 巻号頁・発行日
- vol.28, no.1, pp.67-82, 2015-12-20 (Released:2016-12-14)
- 参考文献数
- 34
- 被引用文献数
- 5
We consider the Bayesian lasso for regression, which can be interpreted as an L1 norm regularization based on a Bayesian approach when the Laplace or doubleexponential prior distribution is placed on the regression coefficients. A crucial issue is an appropriate choice of the values of hyperparameters included in the prior distributions, which essentially control the sparsity in the estimated model. To choose the values of tuning parameters, we introduce a model selection criterion for evaluating a Bayesian predictive distribution for the Bayesian lasso. Numerical results are presented to illustrate the properties of our sparse Bayesian modeling procedure.
- 著者
- Ken Nittono Toshinari Kamakura
- 出版者
- 日本計算機統計学会
- 雑誌
- Journal of the Japanese Society of Computational Statistics (ISSN:09152350)
- 巻号頁・発行日
- vol.14, no.1, pp.31-47, 2001 (Released:2009-12-09)
- 参考文献数
- 13
A modified method for Bayesian image restoration using varying neighborhood structure is proposed. The method reduces computational burden for yielding a restored image due to the dynamical change of structural forms of neighborhood, which should be iteratively and adaptively composed through the process of the restoration calculation. Although, in practice, the results of restoration generally depend on given data, our simulation results show that the method is effective for some given gray-scale images with moderate additive Gaussian noise.