Title	Reconstruction via Bayesian hierarchical models: convexity, sparsity and model reduction
Creator	Calvetti, Daniela
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-06-27T09:31
Description	The reconstruction of sparse signals from indirect, noisy data is a challenging inverse problem. In the Bayesian framework, the sparsity belief can be encoded via hierarchical prior models. In this talk we discuss the convexity - or lack thereof - of the functional associated to different models, and we show that Krylov subspace methods for the computation of the MAP solution implicitly perform an effective and efficient model reduction.
Extent	55.0 minutes
Subject	Mathematics; Functional analysis; Calculus of variations and optimal control; optimization; Numerical analysis
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Case Western Reserve University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-12-25
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0387358
URI	http://hdl.handle.net/2429/72997
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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