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（如果不是全部的话）科学努力的核心。通常情况下，可用的数据比人们希望的更有限，但量化证据的需求同样紧迫。许多统计方法研究，包括这里提出的，都希望开发出能够减轻数据局限性的方法。所面临的限制的常见例子包括一些研究单位的重要变量测量得很差，或者根本没有测量，以及研究单位选择的偏差。本文提出的研究工作旨在进一步理解和实现贝叶斯方法来量化数据中的证据。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性。

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.