摘要翻译:
本文介绍了一种研究后验分布收敛速度的新方法。它是贝叶斯一致性研究的最新方法的自然扩展。特别地,我们改进了包括Dirichlet过程模型和随机Bernstein多项式模型的混合模型的收敛速度。
---
英文标题:
《On rates of convergence for posterior distributions in
infinite-dimensional models》
---
作者:
Stephen G. Walker, Antonio Lijoi, Igor Pr\"unster
---
最新提交年份:
2007
---
分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
--
---
英文摘要:
This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of convergence for models including the mixture of Dirichlet process model and the random Bernstein polynomial model.
---
PDF链接:
https://arxiv.org/pdf/708.1892