collapsed Gibbs sampling


MCMC samplers can often be improved by marginalizing out a subset of the variables in closed form and performing MCMC over the remaining variables. This is more statistically efficient since each particle can cover a larger part of the distribution, and it can also improve mixing by allowing larger jumps.


This concept has the prerequisites:


  • Be able to derive the update rules for collapsed Gibbs sampling
  • Be aware of the motivations in terms of:
    • greater statistical efficiency (from the Rao-Blackwell theorem)
    • faster mixing

Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

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


Machine learning summer school: Markov chain Monte Carlo (2009)
A video tutorial on MCMC methods.
Location: Part 2, 16:47 to 21:37
Author: Iain Murray


See also

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