reversible jump MCMC

(35 minutes to learn)

Summary

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.

Context

This concept has the prerequisites:

Goals

  • 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)

-Paid-

See also

-No Additional Notes-