Approximate Bayesian Inference

被引:10
作者
Alquier, Pierre [1 ]
机构
[1] RIKEN, Ctr Adv Intelligence Project AIP, Tokyo 1030027, Japan
关键词
Bayesian statistics; machine learning; variational approximations; PAC-Bayes; expectation-propagation; Markov chain Monte Carlo; Langevin Monte Carlo; sequential Monte Carlo; Laplace approximations; approximate Bayesian computation; Gibbs posterior; VON-MISES THEOREM; CHAIN MONTE-CARLO; VARIATIONAL INFERENCE; COMPUTATION; MODELS; DISTRIBUTIONS; POSTERIOR; ENTROPY; STATE; MCMC;
D O I
10.3390/e22111272
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference.
引用
收藏
页数:12
相关论文
共 157 条
[1]  
Alemi AA, 2019, VARIATIONAL PREDICTI, P1
[2]   Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels [J].
Alquier, P. ;
Friel, N. ;
Everitt, R. ;
Boland, A. .
STATISTICS AND COMPUTING, 2016, 26 (1-2) :29-47
[3]  
Alquier P., 2016, JMLR, V17, P8374
[4]  
Alquier P., 2020, ARXIV2020200903017
[5]   CONCENTRATION OF TEMPERED POSTERIORS AND OF THEIR VARIATIONAL APPROXIMATIONS [J].
Alquier, Pierre ;
Ridgway, James .
ANNALS OF STATISTICS, 2020, 48 (03) :1475-1497
[6]   Simpler PAC-Bayesian bounds for hostile data [J].
Alquier, Pierre ;
Guedj, Benjamin .
MACHINE LEARNING, 2018, 107 (05) :887-902
[7]  
Amit R., 2018, INTERNATIONAL CONFER, P205
[8]   An introduction to MCMC for machine learning [J].
Andrieu, C ;
de Freitas, N ;
Doucet, A ;
Jordan, MI .
MACHINE LEARNING, 2003, 50 (1-2) :5-43
[9]   THE PSEUDO-MARGINAL APPROACH FOR EFFICIENT MONTE CARLO COMPUTATIONS [J].
Andrieu, Christophe ;
Roberts, Gareth O. .
ANNALS OF STATISTICS, 2009, 37 (02) :697-725
[10]  
[Anonymous], 2017, CoRR