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Activity Number: 368
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319473
Title: Bayesian Inversion of a Large Spatial Field Using Predictive Process
Author(s): Anirban Mondal*
Companies: Case Western Reserve University
Keywords: Spatial Statistics ; Bayesian Inference ; Inverse Problem ; Markov Chain Monte Carlo ; Uncertainty Quantification

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.

Authors who are presenting talks have a * after their name.

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