Monte Carlo estimation

(1.1 hours to learn)


One way to answer queries about a probability distribution is to simulate from the distribution, a procedure known as Monte Carlo estimation. In particular, we estimate the expected value of some function f with respect to a distribution p by generating samples from p and averaging the values of f over those samples.


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: up to 10:20
Author: Iain Murray
Information Theory, Inference, and Learning Algorithms
A graudate-level textbook on machine learning and information theory.
Author: David MacKay


Supplemental resources (the following are optional, but you may find them useful)


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