Bayesian Evidence Synthesis: Exploring the Limits of Limited Data

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。很多时候，可用的数据比人们希望的要有限得多，但量化证据的需要同样紧迫。许多统计方法论研究，包括这里提出的研究，都着眼于开发减轻数据限制的方法论。面临的限制的常见例子包括对一些研究单位的重要变量测量得很差，或者根本没有，以及在选择研究单位方面的偏见。这项拟议的研究工作旨在加深对贝叶斯方法的理解和实施，以从数据中量化证据。贝叶斯范式提供了一个原则性的方案来管理从数据到科学证据的各种不确定因素。

The quantification of evidence contained in data is clearly central to most, if not all, scientific endeavors. 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.