论文标题
使用置信分布桥接贝叶斯，频繁和基准（BFF）推断
Bridging Bayesian, frequentist and fiducial (BFF) inferences using confidence distribution
论文作者
论文摘要
贝叶斯，常见主义者和信托（BFF）推论比在科学界历史上被认为比历史上所感知得多得多（参见Reid and Cox 2015； Kass 2011； Efron 1998）。大多数从业者可能更熟悉两个主要的统计推论范式，贝叶斯推论和常见的推断。 R.A.第三个鲜为人知的基准推论范例率先率先。 Fisher试图定义推断为贝叶斯定理的一种反演程序。尽管每个范式都有其自身的优势和局限性，但要受其不同的哲学基础，但本文旨在通过置信分布理论和蒙特 - 卡洛模拟程序的镜头桥接这些不同的推论方法。本文试图了解这三个不同的范式如何可以统一并在基本层面上进行统一并进行比较，从而增加所有领域的统计理论家和从业者可用的技术范围。
Bayesian, frequentist and fiducial (BFF) inferences are much more congruous than they have been perceived historically in the scientific community (cf., Reid and Cox 2015; Kass 2011; Efron 1998). Most practitioners are probably more familiar with the two dominant statistical inferential paradigms, Bayesian inference and frequentist inference. The third, lesser known fiducial inference paradigm was pioneered by R.A. Fisher in an attempt to define an inversion procedure for inference as an alternative to Bayes' theorem. Although each paradigm has its own strengths and limitations subject to their different philosophical underpinnings, this article intends to bridge these different inferential methodologies through the lenses of confidence distribution theory and Monte-Carlo simulation procedures. This article attempts to understand how these three distinct paradigms, Bayesian, frequentist, and fiducial inference, can be unified and compared on a foundational level, thereby increasing the range of possible techniques available to both statistical theorists and practitioners across all fields.
