Stein's Method of functional central limit theorem

泛函中心极限定理的斯坦因法

• 批准号: 1654155
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1654155
• 关键词: Stein; Method; functional; central; limit

Stein's method is a powerful tool for estimating distributional distances. It has been applied in multivariate, univariate and infinite-dimensional settings, including the Malliavin calculus. However it has been little used for functional approximations, except for some attempts of Barbour (1990) and Barbour and Janson (2009). We would like to develop this theory.Examples of the problems we would like to apply it to include: processes of maxima of partial sums of random variables or the Wright-Fisher diffusions approximation.The project falls within the EPSRC statistics and applied probability research area.

Stein方法是估计分布距离的有力工具。它已被应用于多变量，单变量和无限维的设置，包括Malliavin演算。然而，它很少用于函数近似，除了Barclay（1990）和Barclay and Janson（2009）的一些尝试。我们想发展这个理论。我们想应用它的问题的例子包括：随机变量的部分和的最大值的过程或Wright-Fisher扩散近似。该项目福尔斯EPSRC统计和应用概率研究领域。

项目成果

Note on A. Barbour's paper on Stein's method for diffusion approximations

关于 A. Barbour 关于 Stein 扩散近似方法的论文的注释

• DOI: 10.1214/17-ecp54
• 发表时间: 2017
• 期刊: Electronic Communications in Probability
• 影响因子: 0.5
• 作者: Kasprzak M

Diffusion approximations via Stein's method and time changes

通过 Stein 方法和时间变化进行的扩散近似

• DOI: 10.48550/arxiv.1701.07633
• 发表时间: 2017
• 期刊: arXiv e-prints
• 作者: Kasprzak Mikolaj J.

Stein's method for multivariate Brownian approximations of sums under dependence

依赖项下总和的多元布朗近似的 Stein 方法

• DOI: 10.48550/arxiv.1708.02521
• 发表时间: 2017
• 期刊: arXiv e-prints
• 作者: Kasprzak

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

具有有限数据均值和方差保证的可扩展高斯过程推理

• DOI: 10.48550/arxiv.1806.10234
• 发表时间: 2018
• 期刊: arXiv e-prints
• 作者: Huggins Jonathan H.

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

贝叶斯后验近似误差的实际界限：非渐近方法

• DOI: 10.48550/arxiv.1809.09505
• 发表时间: 2018
• 期刊: arXiv e-prints
• 作者: Huggins Jonathan H.