Computing Bayes: From Then ‘Til Now

IF 3.9 1区数学 Q1 STATISTICS & PROBABILITY

Statistical Science Pub Date : 2022-08-01 DOI:10.1214/22-STS876

G. Martin, David T. Frazier, C. Robert

引用次数: 7

Abstract

This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first confronted by Bayes in 1763, we highlight the key contributions of: Laplace, Metropolis (and, importantly, his co-authors!), Hammersley and Handscomb, and Hastings, all of which set the foundations for the computational revolution in the late 20th century -- led, primarily, by Markov chain Monte Carlo (MCMC) algorithms. A very short outline of 21st century computational methods -- including pseudo-marginal MCMC, Hamiltonian Monte Carlo, sequential Monte Carlo, and the various `approximate' methods -- completes the paper.

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计算贝叶斯：从那时到现在

本文将带领读者穿越贝叶斯计算的历史，从18世纪到今天。从1763年贝叶斯首次遇到的一维积分开始，我们强调了拉普拉斯、大都会（更重要的是，还有他的合著者！）、汉默斯利和汉斯科姆以及黑斯廷斯的关键贡献，所有这些都为20世纪末的计算革命奠定了基础，主要是由马尔可夫链蒙特卡罗（MCMC）算法领导的。本文简要介绍了21世纪的计算方法，包括伪边缘MCMC、哈密顿蒙特卡罗、序列蒙特卡罗和各种“近似”方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Science 数学-统计学与概率论

CiteScore

6.50

自引率

1.80%

发文量

审稿时长

>12 weeks

期刊介绍： The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.