Activity Number:
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368
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #319473
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Title:
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Bayesian Inversion of a Large Spatial Field Using Predictive Process
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Author(s):
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Anirban Mondal*
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Companies:
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Case Western Reserve University
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Keywords:
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Spatial Statistics ;
Bayesian Inference ;
Inverse Problem ;
Markov Chain Monte Carlo ;
Uncertainty Quantification
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Abstract:
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We consider a Bayesian approach to nonlinear inverse problems in which the unknown quantity (input) is a random spatial field. The Bayesian approach contains a natural mechanism for regularization in the form of prior information and casts the inverse solution as a posterior probability distribution. Data from different sources and scales are also integrated using a Bayesian hierarchical model. Predictive Process have been used for a low rank approximation of the spatial process where the original process is projected onto a subspace that is generated by realizations of the original process at a specified set of locations called knot points. The estimation is carried out using Markov chain Monte Carlo method. Numerical results are presented by analyzing simulated as well as real data.
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Authors who are presenting talks have a * after their name.