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SIAMIS
2011

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

8 years 8 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing image acquisition in magnetic resonance scanners, can be addressed by querying the SLM posterior covariance, unrelated to the density’s mode. We propose a scalable algorithmic framework, with which SLM posteriors over full, high-resolution images can be approximated for the first time, solving a variational optimization problem which is convex if and only if posterior mode finding is convex. These methods successfully drive the optimization of sampling trajectories for real-world magnetic resonance imaging through Bayesian experimental design, which has not been attempted before. Our methodology provides new insight into similarities and differences between spar...
Matthias W. Seeger, Hannes Nickisch
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where SIAMIS
Authors Matthias W. Seeger, Hannes Nickisch
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