Bayesian Evidence Synthesis: Exploring the Limits of Limited data

贝叶斯证据合成：探索有限数据的局限性

• 批准号: RGPIN-2019-03957
• 负责人: Gustafson, Paul
• 金额: $ 3.86万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765709
• 关键词: Bayesian; Evidence; Synthesis; Exploring; Limits

The quantification of evidence contained in data is clearly central to most, if not all, scientific endeavours. Very often the available data are more limited than one would like, yet the need to quantify the evidence is no less pressing. A lot of statistical methodological research, including that proposed here, looks to develop methodologies which mitigate limitations of the data. Common examples of the limitations faced include important variables being measured poorly, or not at all, for some study units, and bias in the selection of study units. This proposed research work aims to further the understanding and implementation of Bayesian methods to quantify evidence from data. The Bayesian paradigm offers a principled scheme to manage the various uncertainties that lie on the route from data to scientific evidence.

数据中包含的证据的量化显然是大多数（如果不是全部）科学努力的核心。通常，可用的数据比人们想要的更有限，但是量化证据的需求也同样受到压力。许多统计学研究（包括此处提出的方法）旨在开发减轻数据限制的方法。所面临的局限性的常见示例包括某些研究单位的重要变量的测量很差，或者根本没有测量，以及在选择研究单位中的偏见。这项拟议的研究工作旨在进一步理解和实施贝叶斯方法，以量化数据的证据。贝叶斯范式提供了一个原则性的计划，以管理从数据到科学证据的路线上的各种不确定性。