Randal Douc, Alain Durmus, Aurélien Enfroy, and Jimmy Olson have arXived their Kick-Kac teleportation paper, which was presented by Randal at CIRM last semester. It is based on Kac’s theorem, which states that, for a Markov chain with invariant distribution π, under (π) stationarity, the average tour between two visits to an accessible set C is also stationary. Which can be used for approximating π(h) if π(C) is known (or well-estimated). Jim Hobert and I exploited this theorem in our 2004 perfect sampling paper. The current paper contains a novel proof of the theorem under weaker conditions. (Note that the only condition on C is that it is accessible, rather than a small set. Which becomes necessary for geometric ergodicity, see condition (A4).)
What they define as the Kick-Kac teleportation (KKT) process is the collection of trajectories between two visits to C. Their memoryless version requires perfect simulations from π restricted to the set C. With a natural extension based on a Markov kernel keeping π restricted to the set C stationary. And a further generalisation allowing for lighter tails that also contains the 2005 paper by Brockwell and Kadane as a special case.
The ability of generating from a different kernel Q at each visit to C allows for different dynamics (as in other composite kernels). In their illustrations, the authors use lowest density regions for C, which is rather surprising to me. Except that it allows for a better connection between modes of the target π: the higher performances of the KKT algorithms against the considered alternatives are apparently dependent on the ability of the kernel Q to explore other modes with sufficient frequency.
Xi'an's Og 