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Activity Number:
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247
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302993 |
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Title:
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Multilevel Adaptive Sampling for Inverse Problems
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Author(s):
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Dave Higdon*+ and J. David Moulton
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Companies:
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Los Alamos National Laboratory and Los Alamos National Laboratory
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Address:
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P.O. Box 1663; MS F600, Los Alamos, NM, 87545,
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Keywords:
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multigrid solver ; Markov chain Monte Carlo ; inverse problem
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Abstract:
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Over the past few decades, efficient and robust multilevel solvers have been developed for a variety of applications which range from medical tomography to flow in porous media. Clearly such solvers can be used as a "black box" (within an MCMC scheme, for example) for inferring unknown parameters or initial conditions in inverse problems. While computational efficiency has been the primary motivation for the development of such multilevel solvers, it is hard to resist the temptation of prying into these solvers so that the coarser representations can be used to help guide the posterior sampling. In this talk we explore sequential and Markov chain Monte Carlo methods for exploiting the implicit coarsened representations within a multilevel solver to speed up posterior sampling.
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