An introduction to MCMC for machine learning

被引：1490

作者：

Andrieu, C

de Freitas, N

Doucet, A

Jordan, MI

机构：

[1] Univ Bristol, Dept Math, Stat Grp, Bristol BS8 1TW, Avon, England

[2] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1Z4, Canada

[3] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3052, Australia

[4] Univ Calif Berkeley, Dept Comp Sci, Berkeley, CA 94720 USA

[5] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA

来源：

MACHINE LEARNING | 2003年 / 50卷 / 1-2期

关键词：

Markov chain Monte Carlo; MCMC; sampling; stochastic algorithms;

D O I：

10.1023/A:1020281327116

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.

引用

页码：5 / 43

页数：39

共 140 条

[1] BAYESIAN-ANALYSIS OF BINARY AND POLYCHOTOMOUS RESPONSE DATA [J].