Computational advances for and from Bayesian analysis

被引:29
作者
Andrieu, C [1 ]
Doucet, A
Robert, CP
机构
[1] Univ Bristol, Dept Math, Bristol BS8 1TW, Avon, England
[2] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England
[3] Univ Paris 09, CEREMADE, F-75775 Paris, France
关键词
Monte Carlo methods; importance sampling; Markov chain Monte Carlo (MCMC) algorithms;
D O I
10.1214/088342304000000071
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The emergence in the past years of Bayesian analysis in many methodological and applied fields as the solution to the modeling of complex problems cannot be dissociated from major changes in its computational implementation. We show in this review how the advances in Bayesian analysis and statistical computation are intermingled.
引用
收藏
页码:118 / 127
页数:10
相关论文
共 72 条