Computational methods for Bayesian clustering

贝叶斯聚类的计算方法

• 批准号: 2884420
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884420
• 关键词: Computational; methods; Bayesian; clustering

Bayesian clustering allows to partition data into groups of homogeneous points, while incorporating prior information on the data structure and on the partition itself. However, standard stochastic search methods for Bayesian inference of partitions are often computationally prohibitive. Optimization-based methods instead, while faster, often disregard posterior uncertainty. This project will investigate computationally efficient optimization methods for approximating the posterior distribution on partitions, while accounting for posterior uncertainty.

贝叶斯聚类允许将数据划分为同质点的组，同时结合数据结构和分区本身的先验信息。然而，标准的随机搜索方法的贝叶斯推断的分区往往是计算上的禁止。相反，基于优化的方法虽然更快，但往往忽略了后验不确定性。本计画将研究计算效率高的最佳化方法，以近似分区上的后验分布，同时考虑后验不确定性。