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Bayesian factorization via $L_{1/2}$ shrinkageBayesian factorization via $L_{1/2}$ shrinkage

Shicheng Liu · Qingping Zhou · Yanan Fan · Xiongwen Ke首次提交 2026-03-28 · 修订 2026-03-28 · v1arXiv预印本

基于 L₁/₂ 收缩的贝叶斯因子分解AI 解读

Factor models are widely used for dimension reduction. Bayesian approaches to these models often place a prior on the factor loadings that allows for infinitely many factors, with loadings increasingly shrunk toward zero as the column index increases. However, existing increasing shrinkage priors often possess complex hierarchical structures that complicate posterior inference. To address this issue, we propose using an $L_{1/2}$ shrinkage prior. We demonstrate that by…Factor models are widely used for dimension reduction. Bayesian approaches to these models often place a prior on the factor loadings that allows for infinitely many factors, with loadings increasingly shrunk toward zero as the column index increases. However, existing increasing shrinkage priors often possess complex hierarchical structures that complicate posterior inference. To address this issue, we propose using an $L_{1/2}$ shrinkage prior. We demonstrate that by…

贝叶斯因子模型

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-01

AI 解读阅读原文 ↗BibTeX ↓

Bayesian factorization via L1/2L_{1/2}L1/2​ shrinkageBayesian factorization via $L_{1/2}$ shrinkage

Bayesian factorization via $L_{1/2}$ shrinkageBayesian factorization via $L_{1/2}$ shrinkage