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 journalArticle (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 contributionAn introduction to MCMC for machine learning
Original languageEnglish
Pages (from-to)5-43
Number of pages39
JournalMachine Learning
Volume50
Issue number1-2
DOIs
Publication statusPublished - Jan 2003

Keywords

  • Markov chain Monte Carlo
  • MCMC
  • Sampling
  • Stochastic algorithms

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

Cite this