Department of Statistics, University of Oxford, Oxford, UK.
Department of Mathematics, University of Texas at Austin, Austin, TX, USA.
Philos Trans A Math Phys Eng Sci. 2023 May 15;381(2247):20220143. doi: 10.1098/rsta.2022.0143. Epub 2023 Mar 27.
In this paper, we start by reviewing exchangeability and its relevance to the Bayesian approach. We highlight the predictive nature of Bayesian models and the symmetry assumptions implied by beliefs of an underlying exchangeable sequence of observations. By taking a closer look at the Bayesian bootstrap, the parametric bootstrap of Efron and a version of Bayesian thinking about inference uncovered by Doob based on martingales, we introduce a parametric Bayesian bootstrap. Martingales play a fundamental role. Illustrations are presented as is the relevant theory. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
在本文中,我们首先回顾了可交换性及其与贝叶斯方法的相关性。我们强调了贝叶斯模型的预测性质,以及对基础可交换观测序列的信念所隐含的对称假设。通过仔细研究贝叶斯引导抽样、Efron 的参数引导抽样以及 Doob 基于鞅提出的一种贝叶斯推理方法,我们引入了参数贝叶斯引导抽样。鞅起着至关重要的作用。本文提供了实例和相关理论。本文是“贝叶斯推理:挑战、视角和前景”主题专刊的一部分。