Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation

被引：1

作者：

Wu Changye ^{[1
]}

Robert, Christian P. ^{[1
,2
]}

机构：

[1] Univ Paris Dauphine PSL, Paris, France

[2] Univ Warwick, Coventry, W Midlands, England

来源：

CASE STUDIES IN APPLIED BAYESIAN DATA SCIENCE: CIRM JEAN-MORLET CHAIR, FALL 2018 | 2020年 / 2259卷

关键词：

Monte Carlo methods; MCMC algorithms; Bouncy particle sampler; PDMP; Big Data; COVARIANCE STRUCTURE; GIBBS SAMPLER;

D O I：

10.1007/978-3-030-42553-1_4

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This chapter briefly recalls the major simulation based methods for conducting Bayesian computation, before focusing on partly deterministic Markov processes and a novel modification of the bouncy particle sampler that offers an interesting alternative when dealing with large datasets.

引用

页码：89 / 119

页数：31

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