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Title	High-dimensional Bayesian inference and convex geometry: theory, methods, and algorithms
Creator	Pereyra, Marcelo
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-11-14T09:40
Description	This presentation summarises some new developments in theory, methods, and algorithms for performing Bayesian inference in high-dimensional models that are log-concave, with application to mathematical and computational imaging in convex settings. These include new efficient stochastic simulation and optimisation Bayesian computation methods that tightly combine proximal convex optimisation with Markov chain Monte Carlo techniques; strategies for estimating unknown model parameters and performing model selection, methods for calculating Bayesian confidence intervals for images and performing uncertainty quantification analyses; and new theory regarding the role of convexity in maximum-a-posteriori and minimum-mean-square-error estimation. The new theory, methods, and algorithms are illustrated with a range of mathematical imaging experiments.
Extent	37.0
Subject	Mathematics Statistics Statistical mechanics, structure of matter Statistical mechanics
Geographic Location	Oaxaca (Mexico : State)
Type	Moving Image
FileFormat	video/mp4
Language	eng
Notes	Author affiliation: Heriot Watt University
Series	BIRS Workshop Lecture Videos (Oaxaca (Mexico : State))
Date Available	2019-05-14
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0378706
URI	http://hdl.handle.net/2429/70150
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Researcher
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
AggregatedSourceRepository	DSpace

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