Bayesian structure selection approaches for multiple binary responses via multi-task learning
- 2024-05-06 (Mon.), 10:30 AM
- 統計所B1演講廳;茶 會:上午10:10。
- 中文演講,實體與線上視訊同步進行。
- Prof. Chi-Hsiang Chu ( 朱基祥 助理教授 )
- 國立高雄大學統計學研究所
Abstract
In this work, we concentrate on the Bayesian structure selection problems for the categorical response. We focus on solving the selection problem for multiple binary responses and use the probit model for each response. Here, we consider the group structure with sparsity property on the rows of coefficient matrix where each row corresponds to one variable. Then we identify the relevant variables for the responses and the selection problems can be treated as the multi-task learning problem. The effectiveness of our proposed method will be demonstrated through simulation studies.
Keyword: Multi-task learning, component-wise algorithm, group structure, Markov chain Monte Carlo
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最後更新日期:2024-04-29 14:29