New Developments in Nonparametric Bayesian Inference; Univariate and Multivariate time series with infinite varaince.

非参数贝叶斯推理的新进展；

• 批准号: 203276-2013
• 负责人: Zarepour, Mahmoud
• 金额: $ 0.8万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=523414
• 关键词: New; Developments; Nonparametric; Bayesian; Inference

The applicant's research program is concentrated on two major subjects in statistical inference. The first subject is mainly related to Bayesian inference. In the Bayesian paradigm, the statisticians considers having a probabilistic prior knowledge on some unknown parameters. Later, the collected data is used to update their prior knowledge under the chosen model. A nonparametric Bayesian model considers the prior knowledge in a much more general framework with minimal restrictions. The applicant's research area in this field is mostly related to the construction of new processes used in nonparametric Bayesian models and the derivation of fast yet more precise approximation tools for these processes to use them in statistical inference. The applicant also derives efficient simulation techniques for such processes in order to enable practitioners to run their computer programs with faster speed.

申请人的研究计划集中在统计推断的两个主要课题上。第一个主题主要涉及贝叶斯推理。在贝叶斯范式中，统计学家认为对一些未知参数具有概率先验知识。之后，收集的数据用于更新所选模型下的先验知识。非参数贝叶斯模型在一个具有最小限制的更一般的框架中考虑先验知识。申请人在这一领域的研究领域主要涉及非参数贝叶斯模型中使用的新过程的构建，以及为这些过程导出快速但更精确的近似工具，以将其用于统计推断。申请人还导出了用于这种过程的有效模拟技术，以便使从业者能够以更快的速度运行他们的计算机程序。