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Involutive MCMC: a Unifying Framework
Kirill Neklyudov · Max Welling · Evgenii Egorov · Dmitry Vetrov

Wed Jul 15 01:00 PM -- 01:45 PM & Thu Jul 16 01:00 AM -- 01:45 AM (PDT) @

Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad spectrum of algorithms, varying in the way they are motivated, the way they are applied and how efficiently they sample. Despite all the differences, many of them share the same core principle, which we unify as the Involutive MCMC (iMCMC) framework. Building upon this, we describe a wide range of MCMC algorithms in terms of iMCMC, and formulate a number of "tricks" which one can use as design principles for developing new MCMC algorithms. Thus, iMCMC provides a unified view of many known MCMC algorithms, which facilitates the derivation of powerful extensions. We demonstrate the latter with two examples where we transform known reversible MCMC algorithms into more efficient irreversible ones.

Author Information

Kirill Neklyudov (Samsung)
Max Welling (University of Amsterdam & Qualcomm)
Evgenii Egorov (Skolkovo Institute of Science and Technology)
Dmitry Vetrov (Higher School of Economics, Samsung AI Center Moscow)

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