slice sampling

(1.2 hours to learn)


Slice sampling is a method for sampling from a one-dimensional probability distribution by doing Gibbs sampling in an auxiliary variable model. A major virtue is that it doesn't require specifying a step size. For this reason, it's a useful tool for constructing MCMC samplers which don't require tuning step size parameters.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Information Theory, Inference, and Learning Algorithms
A graudate-level textbook on machine learning and information theory.
Author: David MacKay
Machine learning summer school: Markov chain Monte Carlo (2009)
A video tutorial on MCMC methods.
Location: Part 2, 22:58 to 39:44
Author: Iain Murray

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


Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Author: David Barber


See also