Geometric MCMC for infinite-dimensional inverse problems

Geometric MCMC for infinite-dimensional inverse problems

Bayesian inverse problems often involve sampling posterior distributions on infinite-dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are characterized by deteriorating mixing times upon meshrefinement, when the finite-dimensional approximations become more accurat...

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Bibliographic Details
Main Authors:	Beskos, A, Girolami, M, Lan, S, Farrell, P, Stuart, A
Format:	Journal article
Published:	Elsevier 2016

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