Computational methods for Bayesian clustering

贝叶斯聚类允许将数据划分为同质点组，同时结合数据结构和划分本身的先验信息。然而，标准的随机搜索方法用于贝叶斯分区推理通常在计算上令人望而却步。基于优化的方法虽然更快，但往往忽略了后验不确定性。本项目将研究计算效率的优化方法，以近似分区上的后验分布，同时考虑后验不确定性。

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.