High-dimensional and spatial data analysis by Bayesian inference

通过贝叶斯推理进行高维空间数据分析

• 批准号: 2295266
• 金额: --
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2295266
• 关键词: dimensional; spatial; data; analysis; Bayesian

Project summary: Whereas (1) standard regression models such as mean regression (ordinary least-squares regression) have been widely applied in many fields such as economics, genetics, ecology and environment and attracted huge amount of attention in literature, and (2) Bayesian inference is one of popular methods recent decades, there is relatively little work on Bayesian inference of regression models beyond mean for problems in those fields.This project aims to: develop novel methods of Bayesian autoregressive beyond mean, propose new single-index models for high-dimensional data analysis, explore effective spatial regression beyond mean, compare different algorithms for Bayesian inference of regression models above.

项目摘要：虽然（1）均值回归（普通最小二乘回归）等标准回归模型已广泛应用于经济学、遗传学、生态学和环境等许多领域，并引起了文献的广泛关注，并且（2）贝叶斯推理是近几十年来流行的方法之一，但针对这些领域的问题对超出均值的回归模型进行贝叶斯推理的工作相对较少。 超越均值的贝叶斯自回归，提出用于高维数据分析的新单指标模型，探索超越均值的有效空间回归，比较上述回归模型的贝叶斯推理的不同算法。