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Sacred and Profane: From the Involutive Theory of MCMC to Helpful Hamiltonian Hacks
Book chapter

Sacred and Profane: From the Involutive Theory of MCMC to Helpful Hamiltonian Hacks

Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis, Cecilia F. Mondaini and Ami Sheth
Handbook of Markov Chain Monte Carlo, pp 418-442
2026

Abstract

Geyer and Neal's landmark chapters in the First Edition of this Handbook provide distinct yet seemingly related views on MCMC algorithms that leverage deterministic dynamics. We review a recently established theory of involutive MCMC and show that this theory provides a natural measure theoretic context that ties together the Metropolis-Hastings-Green and Hamiltonian Monte Carlo (HMC) algorithms respectively discussed by Geyer and Neal. We show that the involutive theory also provides rigorous bases for deterministic MCMC over general (infinite-dimensional) state spaces and surrogate-trajectory HMC algorithms that use approximate dynamics but leave targets invariant. Far from being an end-all be-all theory of MCMC, the involutive theory also provides opportunities for rigorous algorithmic development, a fact we suggest here using a recently established theory of multiproposal MCMC. We illustrate these methods with examples from Bayesian phylogenetics, dimension reduction and inverse problems, emphasizing implementational considerations and scope for future research.

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