Particle EM for Variable Selection
用于变量选择的粒子 EM 算法AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究通用贝叶斯变量选择的多峰优化算法，未指向所列应用领域细分。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
用于变量选择的粒子 EM 算法
- 研究问题
- 尖峰厚尾先验下的贝叶斯变量选择后验具有多个局部峰值时，如何减少 EM 的局部陷阱，并概括模型选择不确定性？
- 方法与思路
- 作者提出由相互排斥粒子组成的群体 EM 搜索，同时给出探索退火后验序列的序贯蒙特卡洛变体，以捕捉多个模式。
- 材料陈述的贡献
- 摘要称，粒子 EM 可通过重要模式及其权重确定性重构后验以近似贝叶斯推断，渐近分析提示稀疏情形不必使用大量粒子。
- 阅读关注点
- 应核对粒子相互作用、模式权重与后验重构的计算方式，以及粒子数结论的条件和对遗漏模式的敏感性。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要；摘要未提供全局最优保证、近似误差界或实际粒子数，不能将模式重构视为精确后验或推断刊本变化。
原文摘要
Despite its long history of success, the EM algorithm has been vulnerable to local entrapment when the posterior/likelihood is multi-modal. This is particularly pronounced in spike-and-slab posterior distributions for Bayesian variable selection. The main thrust of this article is to introduce the particle EM algorithm, a new population-based optimization strategy that harvests multiple modes in search spaces that present many local maxima. Motivated by nonparametric variational Bayes strategies, particle EM achieves this goal by deploying an ensemble of interactive repulsive particles. These particles are geared toward uncharted areas of the posterior, providing a more comprehensive summary of its topography than simple parallel EM deployments. A sequential Monte Carlo variant of particle EM is also proposed that explores a sequence of annealed posteriors by sampling from a set of mutually avoiding particles. Particle EM outputs a deterministic reconstruction of the posterior distribution for approximate fully Bayes inference by capturing its essential modes and mode weights. This reconstruction reflects model selection uncertainty and is supported by asymptotic considerations, which indicate that the requisite number of particles need not be large in the presence of sparsity (when p > n). Supplementary materials for this article are available online.