An introduction to MCMC for machine learning

Christophe Andrieu; Nando De Freitas; Arnaud Doucet; Michael I. Jordan

doi:10.1023/A:1020281327116

An introduction to MCMC for machine learning

Christophe Andrieu^*, Nando De Freitas, Arnaud Doucet, Michael I. Jordan

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

1331 Citations (Scopus)

Abstract

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.

Translated title of the contribution	An introduction to MCMC for machine learning
Original language	English
Pages (from-to)	5-43
Number of pages	39
Journal	Machine Learning
Volume	50
Issue number	1-2
DOIs	https://doi.org/10.1023/A:1020281327116
Publication status	Published - Jan 2003

Keywords

Markov chain Monte Carlo
MCMC
Sampling
Stochastic algorithms

Access to Document

10.1023/A:1020281327116

Link to publication in Scopus

Fingerprint Dive into the research topics of 'An introduction to MCMC for machine learning'. Together they form a unique fingerprint.

Cite this

@article{e2544badcbd543a481cc0bdf45041dc4,

title = "An introduction to MCMC for machine learning",

abstract = "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.",

keywords = "Markov chain Monte Carlo, MCMC, Sampling, Stochastic algorithms",

author = "Christophe Andrieu and {De Freitas}, Nando and Arnaud Doucet and Jordan, {Michael I.}",

year = "2003",

month = jan,

doi = "10.1023/A:1020281327116",

language = "English",

volume = "50",

pages = "5--43",

journal = "Machine Learning",

issn = "0885-6125",

publisher = "Springer Verlag",

number = "1-2",

}

TY - JOUR

T1 - An introduction to MCMC for machine learning

AU - Andrieu, Christophe

AU - De Freitas, Nando

AU - Doucet, Arnaud

AU - Jordan, Michael I.

PY - 2003/1

Y1 - 2003/1

N2 - 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.

AB - 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.

KW - Markov chain Monte Carlo

KW - MCMC

KW - Sampling

KW - Stochastic algorithms

UR - http://www.scopus.com/inward/record.url?scp=0037262814&partnerID=8YFLogxK

U2 - 10.1023/A:1020281327116

DO - 10.1023/A:1020281327116

M3 - Article (Academic Journal)

VL - 50

SP - 5

EP - 43

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

IS - 1-2

ER -