Markov chain Monte Carlo

(1.2 hours to learn)


Markov Chain Monte Carlo (MCMC) is a set of techniques for approximately sampling from a probability distribution p by running a Markov chain which has p as its stationary distribution. Gibbs sampling and Metropolis-Hastings are the most common examples.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Author: Daphne Koller
Other notes:
  • Click on "Preview" to see the videos.
Machine learning summer school: Markov chain Monte Carlo (2009)
A video tutorial on MCMC methods.
Location: 29:08 to 69:40
Author: Iain Murray
Information Theory, Inference, and Learning Algorithms
A graudate-level textbook on machine learning and information theory.
Author: David MacKay


See also