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

1479 Citations (Scopus)


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
Issue number1-2
Publication statusPublished - Jan 2003


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


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