2021/10/18 by Kaito Shimamura, Shuichi Kawano, Shimamura, Kaito +1
Mathematics · Decision Sciences · Biochemistry, Genetics and Molecular Biology · #Statistical Methods and Inference #Grey System Theory Applications #Genetic and phenotypic traits in livestock
paper · pdf · doi:10.48550/arxiv.2110.09040
Network lasso is a method for solving a multi-task learning problem through the regularized maximum likelihood method. A characteristic of network lasso is setting a different model for each sample. The relationships among the models are represented by relational coefficients. A crucial issue in network lasso is to provide appropriate values for these relational coefficients. In this paper, we propose a Bayesian approach to solve multi-task learning problems by network lasso. This approach allows us to objectively determine the relational coefficients by Bayesian estimation. The effectiveness of the proposed method is shown in a simulation study and a real data analysis.