Machine Learning

, Volume 50, Issue 1, pp 5-43

First online:

An Introduction to MCMC for Machine Learning

  • Christophe AndrieuAffiliated withDepartment of Mathematics, Statistics Group, University of Bristol
  • , Nando de FreitasAffiliated withDepartment of Computer Science, University of British Columbia
  • , Arnaud DoucetAffiliated withDepartment of Electrical and Electronic Engineering, University of Melbourne
  • , Michael I. JordanAffiliated withDepartments of Computer Science and Statistics, University of California at Berkeley


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 Carlo MCMC sampling stochastic algorithms