Johnathan M. Bardsley

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heikki haarioprofessor, LUTVerified email at lut.fi
Antti SolonenResearcher, laboratory of applied mathematics, Lappeenranta University of TechnologyVerified email at lut.fi
Tiangang CuiUniversity of SydneyVerified email at sydney.edu.au
James NagyProfessor of Mathematics and Computer Science, Emory UniversityVerified email at emory.edu
Youssef MarzoukProfessor, Massachusetts Institute of TechnologyVerified email at mit.edu
Marko LaineResearch Professor, Finnish Meteorological InstituteVerified email at fmi.fi
Zheng WangMassachusetts Institute of TechnologyVerified email at mit.edu
Stuart M. JefferiesProfessor , Georgia State UniversityVerified email at gsu.edu
Robert PlemmonsReynolds Professor of Computer Science and Mathematics, Emeritus Wake Forest UniversityVerified email at wfu.edu
Albert E ParkerMontana State UniversityVerified email at math.montana.edu
Andrew M StuartComputing + Mathematical Sciences, CaltechVerified email at caltech.edu
Sergios AgapiouDepartment of Mathematics and Statistics, University of CyprusVerified email at ucy.ac.cy
Omiros PapaspiliopoulosBocconiVerified email at unibocconi.it
Per Christian HansenProfessor of Scientific Computing, Technical University of DenmarkVerified email at dtu.dk
Daniela CalvettiCase Western Reserve UniversityVerified email at case.edu
Erkki SomersaloProfessor of Mathematics, Case Western Reserve UniversityVerified email at case.edu
Marco DonatelliDepartment of Science and High Technology, Insubria UniversityVerified email at uninsubria.it
Colin FoxUniversity of OtagoVerified email at otago.ac.nz
Jari KaipioUniversity of AucklandVerified email at math.auckland.ac.nz
Aku SeppänenUniversity of Eastern FinlandVerified email at uef.fi

Johnathan M. Bardsley

Professor of Mathematics, University of Montana

Verified email at mso.umt.edu - Homepage

Inverse Problems Uncertainty Quantification Computational Mathematics Computational Statistics


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Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems JM Bardsley, A Solonen, H Haario, M Laine SIAM Journal on Scientific Computing 36 (4), A1895-A1910, 2014	156	2014
MCMC-BASED IMAGE RECONSTRUCTION WITH UNCERTAINTY QUANTIFICATION JM BARDSLEY SIAM Journal on Scientific Computing 34 (3), A1316–A1332, 2012	129	2012
Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation JM Bardsley, J Goldes Inverse Problems 25 (9), 095005, 2009	124	2009
A nonnegatively constrained convex programming method for image reconstruction JM Bardsley, CR Vogel SIAM Journal on Scientific Computing 25 (4), 1326-1343, 2004	107	2004
Total variation-penalized Poisson likelihood estimation for ill-posed problems JM Bardsley, A Luttman Advances in Computational Mathematics 31 (1), 35-59, 2009	99	2009
Computational Uncertainty Quantification for Inverse Problems JM Bardsley Society for Industrial and Applied Mathematics, 2018	93	2018
Covariance-preconditioned iterative methods for nonnegatively constrained astronomical imaging JM Bardsley, JG Nagy SIAM journal on matrix analysis and applications 27 (4), 1184-1197, 2006	93	2006
A computational method for the restoration of images with an unknown, spatially-varying blur J Bardsley, S Jefferies, J Nagy, R Plemmons Optics express 14 (5), 1767-1782, 2006	86	2006
Analysis of the Gibbs sampler for hierarchical inverse problems S Agapiou, JM Bardsley, O Papaspiliopoulos, AM Stuart SIAM/ASA Journal on Uncertainty Quantification 2 (1), 511-544, 2014	61	2014
Wavefront reconstruction methods for adaptive optics systems on ground-based telescopes JM Bardsley SIAM Journal on Matrix Analysis and Applications 30 (1), 67-83, 2008	48	2008
GAUSSIAN MARKOV RANDOM FIELD PRIORS FOR INVERSE PROBLEMS JM Bardsley Inverse Problems and Imaging 7 (2), 397 - 416, 2013	46	2013
Tikhonov regularized Poisson likelihood estimation: theoretical justification and a computational method JM Bardsley, N Laobeul Inverse Problems in Science and Engineering 16 (2), 199-215, 2008	46	2008
Least-squares methods with Poissonian noise: Analysis and comparison with the Richardson-Lucy algorithm R Vio, J Bardsley, W Wamsteker Astronomy & Astrophysics 436 (2), 741-755, 2005	45	2005
The variational Kalman filter and an efficient implementation using limited memory BFGS H Auvinen, JM Bardsley, H Haario, T Kauranne International Journal for Numerical Methods in Fluids 64 (3), 314-335, 2010	44	2010
Bayesian Inverse Problems with Priors: A Randomize-Then-Optimize Approach Z Wang, JM Bardsley, A Solonen, T Cui, YM Marzouk SIAM Journal on Scientific Computing 39 (5), S140-S166, 2017	43	2017
An efficient computational method for total variation-penalized Poisson likelihood estimation JM Bardsley Inverse Problems and Imaging 2 (2), 167-185, 2008	43	2008
Hierarchical regularization for edge-preserving reconstruction of PET images JM Bardsley, D Calvetti, E Somersalo Inverse Problems 26 (3), 035010, 2010	40	2010
Structured linear algebra problems in adaptive optics imaging JM Bardsley, S Knepper, J Nagy Advances in Computational Mathematics 35 (2), 103-117, 2011	35	2011
Computational methods for a large-scale inverse problem arising in atmospheric optics L Gilles, CR Vogel, JM Bardsley Inverse Problems 18 (1), 237, 2002	32	2002
Laplace-distributed increments, the Laplace prior, and edge-preserving regularization JM Bardsley Journal of Inverse and Ill-Posed Problems 20 (3), 271-285, 2012	30	2012

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