Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101372
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dc.contributor.authorHoang, Viet Ha.en
dc.contributor.authorSchwab, Christoph.en
dc.contributor.authorStuart, Andrew M.en
dc.date.accessioned2014-01-27T07:02:17Zen
dc.date.accessioned2019-12-06T20:37:23Z-
dc.date.available2014-01-27T07:02:17Zen
dc.date.available2019-12-06T20:37:23Z-
dc.date.copyright2013en
dc.date.issued2013en
dc.identifier.citationHoang, V. H., Schwab, C., & Stuart, A. M. (2013). Complexity analysis of accelerated MCMC methods for Bayesian inversion. Inverse Problems, 29(8).en
dc.identifier.urihttps://hdl.handle.net/10356/101372-
dc.identifier.urihttp://hdl.handle.net/10220/18708en
dc.description.abstractThe Bayesian approach to inverse problems, in which the posterior probability distribution on an unknown field is sampled for the purposes of computing posterior expectations of quantities of interest, is starting to become computationally feasible for partial differential equation (PDE) inverse problems. Balancing the sources of error arising from finite-dimensional approximation of the unknown field, the PDE forward solution map and the sampling of the probability space under the posterior distribution are essential for the design of efficient computational Bayesian methods for PDE inverse problems. We study Bayesian inversion for a model elliptic PDE with an unknown diffusion coefficient. We provide complexity analyses of several Markov chain Monte Carlo (MCMC) methods for the efficient numerical evaluation of expectations under the Bayesian posterior distribution, given data δ. Particular attention is given to bounds on the overall work required to achieve a prescribed error level ε. Specifically,we first bound the computational complexity of ‘plain’ MCMC, based on combining MCMC sampling with linear complexity multi-level solvers for elliptic PDE. Our (new) work versus accuracy bounds show that the complexity of this approach can be quite prohibitive. Two strategies for reducing the computational complexity are then proposed and analyzed: first, a sparse, parametric and deterministic generalized polynomial chaos (gpc) ‘surrogate’ representation of the forward response map of the PDE over the entire parameter space, and, second, a novel multi-level Markov chainMonte Carlo strategy which utilizes sampling from a multi-level discretization of the posterior and the forward PDE. For both of these strategies, we derive asymptotic bounds on work versus accuracy, and hence asymptotic bounds on the computational complexity of the algorithms. In particular, we provide sufficient conditions on the regularity of the unknown coefficients of the PDE and on the approximation methods used, in order for the accelerations of MCMC resulting from these strategies to lead to complexity reductions over ‘plain’ MCMC algorithms for the Bayesian inversion of PDEs.en
dc.format.extent38 p.en
dc.language.isoenen
dc.relation.ispartofseriesInverse problemsen
dc.rights© 2013 IOP Publishing Ltd.en
dc.subjectDRNTU::Science::Mathematics::Discrete mathematics::Algorithmsen
dc.titleComplexity analysis of accelerated MCMC methods for Bayesian inversionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.identifier.doi10.1088/0266-5611/29/8/085010en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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