MCMC convergence

(1.3 hours to learn)


Markov chain Monte Carlo (MCMC) samplers eventually converge to their stationary distribution, but they may take a long time to do so. The "mixing time" of a chain refers to how long a chain must be run in order for one sample to be independent of another. Diagnosing mixing time is important for judging the reliability of estimates obtained from an MCMC algorithm.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Machine learning summer school: Markov chain Monte Carlo (2009)
A video tutorial on MCMC methods.
Location: Part 2, 2:53 to 18:11
Author: Iain Murray
Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Author: Daphne Koller
Other notes:
  • Click on "Preview" to see the videos.


See also

-No Additional Notes-