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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:title tba - Tony Lelievre (ENPC - École des Ponts 
 ParisTech)
DTSTART;TZID=Europe/London:20191120T135000
DTEND;TZID=Europe/London:20191120T143000
UID:TALK135037AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/135037
DESCRIPTION:<span>Various applications require the sampling of
  probability measures restricted to submanifolds d
 efined as the level set of some functions\, in par
 ticular in computational statistical physics. We w
 ill present recent results on so-called Hybrid Mon
 te Carlo methods\, which consists in adding an ext
 ra momentum variable to the state of the system\, 
 and discretizing the associated Hamiltonian dynami
 cs with some stochastic perturbation in the extra 
 variable. In order to avoid biases in the invarian
 t probability measures sampled by discretizations 
 of these stochastically perturbed Hamiltonian dyna
 mics\, a Metropolis rejection procedure can be con
 sidered. The so-obtained scheme belongs to the cla
 ss of generalized Hybrid Monte Carlo (GHMC) algori
 thms\, and we will discuss how to ensure that the 
 sampling method is unbiased in practice.<br> <br> 
 References:<br> - T. Leli&egrave\;vre\, M. Rousset
  and G. Stoltz\, Langevin dynamics with constraint
 s and computation of free energy differences\, Mat
 hematics of Computation\, 81(280)\, 2012.<br> - T.
  Leli&egrave\;vre\, M. Rousset and G. Stoltz\, Hyb
 rid Monte Carlo methods for sampling probability m
 easures on submanifolds\, to appear in Numerische 
 Mathematik\, 2019.<br> - E. Zappa\, M. Holmes-Cerf
 on\, and J. Goodman. Monte Carlo on manifolds: sam
 pling densities and integrating functions. Communi
 cations in Pure and Applied Mathematics\, 71(12)\,
  2018.</span><br><br><br><br><br>
LOCATION:Seminar Room 2\, Newton Institute
CONTACT:INI IT
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