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Computational Mathematics Seminar - Victor Churchill

Computational Mathematics Seminar
December 3, 2019
1:00PM - 2:00PM
Math Tower 154

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Add to Calendar 2019-12-03 13:00:00 2019-12-03 14:00:00 Computational Mathematics Seminar - Victor Churchill Title: High Order Total Variation Bayesian Learning via Synthesis Speaker: Victor Churchill - Dartmouth College Abstract: We present a sparse Bayesian learning algorithm for inverse problems in signal and image processing with a high order total variation sparsity prior that can provide both accurate estimation as well as uncertainty quantification. Sparse Bayesian learning often produces more accurate estimates than the typical maximum a posteriori Bayesian estimates for sparse signal recovery. In addition, it also provides a full posterior distribution which aids downstream processing and uncertainty quantification. However, sparse Bayesian learning is only available to problems with a direct sparsity prior or those formed via synthesis. We build upon a recent paper to demonstrate how both 1D and 2D problems with a high order total variation sparsity prior can be formulated via synthesis, and develop a synthesis-based total variation Bayesian learning algorithm. Numerical examples are provided to demonstrate how our new technique is effectively employed. Seminar Link Math Tower 154 Department of Mathematics math@osu.edu America/New_York public

Title: High Order Total Variation Bayesian Learning via Synthesis

Speaker: Victor Churchill - Dartmouth College

Abstract: We present a sparse Bayesian learning algorithm for inverse problems in signal and image processing with a high order total variation sparsity prior that can provide both accurate estimation as well as uncertainty quantification. Sparse Bayesian learning often produces more accurate estimates than the typical maximum a posteriori Bayesian estimates for sparse signal recovery. In addition, it also provides a full posterior distribution which aids downstream processing and uncertainty quantification. However, sparse Bayesian learning is only available to problems with a direct sparsity prior or those formed via synthesis. We build upon a recent paper to demonstrate how both 1D and 2D problems with a high order total variation sparsity prior can be formulated via synthesis, and develop a synthesis-based total variation Bayesian learning algorithm. Numerical examples are provided to demonstrate how our new technique is effectively employed.

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