Machine Learning

, Volume 50, Issue 1, pp 5–43

An Introduction to MCMC for Machine Learning


  • Christophe Andrieu
    • Department of Mathematics, Statistics GroupUniversity of Bristol
  • Nando de Freitas
    • Department of Computer ScienceUniversity of British Columbia
  • Arnaud Doucet
    • Department of Electrical and Electronic EngineeringUniversity of Melbourne
  • Michael I. Jordan
    • Departments of Computer Science and StatisticsUniversity of California at Berkeley

DOI: 10.1023/A:1020281327116

Cite this article as:
Andrieu, C., de Freitas, N., Doucet, A. et al. Machine Learning (2003) 50: 5. doi:10.1023/A:1020281327116


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.

Markov chain Monte CarloMCMCsamplingstochastic algorithms
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© Kluwer Academic Publishers 2003