Acceleration of MCMC methods by non-reversibility and factorization
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Amphi Recherche
LPC
During this talk, I will present non-reversible Markov-chain
Monte Carlo methods, based on piecewise deterministic Markov
processes (PDMP). First developed for multiparticle systems, the
goal was to emulate the successes of cluster algorithms for spin
systems and was achieved through the replacement of the time
reversibility by symmetries of the sampled probability
distribution itself. These methods have shown to bring clear
accelerations and are now competing with molecular dynamics
methods in chemical physics or state-of-the-art sampling schemes,
e.g. Hamiltonian Monte Carlo, in statistical inference. Finally, I
will explain how the factorization of interaction terms can lead
to computational complexity reduction, for instance in presence of
long-range interactions.