Approximate exchangeability and de Finetti priors in 2022
这是一篇综述，回顾了德菲内蒂关于部分可交换性和近似可交换性的工作，以及他在非标准情形下设定信息先验的方法，并补充了马尔可夫链蒙特卡洛方法的最新进展和收敛速度的严格界限。
Abstract This is a review paper, beginning with de Finetti's work on partial exchangeability, continuing with his approach to approximate exchangeability, and then his (surprising) approach to assigning informative priors in nonstandard situations. Recent progress on Markov chain Monte Carlo methods for drawing conclusions is supplemented by a review of work by Gerencsér and Ottolini on getting honest bounds for rates of convergence. The paper concludes with a speculative approach to combining classical asymptotics with Monte Carlo. This promises real speed‐ups and makes a nice example of how theory and computation can interact.