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VERSION:2.0
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BEGIN:VEVENT
DTSTAMP:20261020T043609Z
LAST-MODIFIED:20180301T140604Z
DTSTART:20180302T170000Z
DTEND:20180302T180000Z
UID:event1931@bu.edu
URL:http://physics.bu.edu/internal/events/show/1931
SUMMARY:"Unbiased Markov chain Monte Carlo with couplings joint work with J
	ohn O'Leary\, Yves F. Atchadé"
DESCRIPTION:Featuring Pierre E. Jacob\, Harvard University\n\nPart of the C
	ondensed Matter Theory Seminar Series.\n\nMarkov chain Monte Carlo (MCMC) m
	ethods provide consistent approximations of integrals as the number of iter
	ations goes to infinity. MCMC estimators are generally biased after any fix
	ed number of iterations\, which complicates both parallel computation and t
	he construction of confidence intervals. We propose to remove this bias by 
	using couplings of Markov chains together with a telescopic sum argument of
	 Glynn & Rhee (2014). The resulting unbiased estimators can be computed in 
	parallel\, with confidence intervals following directly from the Central Li
	mit Theorem for i.i.d. variables. We discuss practical couplings for popula
	r algorithms such as Metropolis-Hastings\, Gibbs samplers\, and Hamiltonian
	 Monte Carlo. We establish the theoretical validity of the proposed estimat
	ors and study their efficiency relative to the underlying MCMC algorithms. 
	Finally\, we illustrate the performance and limitations of the method on va
	rious examples\, involving discrete and continuous high dimensional state s
	paces.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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