reversible jump MCMC

(35 minutes to learn)


Reversible jump MCMC is a special case of Metropolis-Hastings where proposals are made between continuous spaces of differing dimensionality. The most common use is in Bayesian model averaging.


This concept has the prerequisites:


  • Understand why generic MCMC operators aren't applicable when sampling over spaces of differing dimensionality.
  • Know the basic idea behind reversible jump: augmenting the parameter spaces so that they are equal dimension.
  • Derive the acceptance probability for the proposal. The tricky part is dealing with the fact that part of the proposal distribution is deterministic.

Core resources (read/watch one of the following)


See also

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