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均匀点的组，同时纳入有关数据结构和分区本身的先前信息。但是，用于分区的贝叶斯推断的标准随机搜索方法通常在计算上是过敏的。相反，基于优化的方法虽然更快，但通常会忽略后部不确定性。该项目将研究计算有效的优化方法，以近似分区的后验分布，同时考虑后验不确定性。