Víctor Elvira

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Luca MartinoAssociate ProfessorVerified email at urjc.es
David LuengoUniversidad Politécnica de Madrid (UPM)Verified email at upm.es
Ignacio SantamariaProfessor, Electrical Engineering, University of CantabriaVerified email at unican.es
Joaquin MiguezDepartment of Signal Theory & Communications, Universidad Carlos III de MadridVerified email at tsc.uc3m.es
Emilie ChouzenouxFull researcher, Inria Saclay, FranceVerified email at centralesupelec.fr
Gustau Camps-VallsProfessor. Fellow IEEE/ELLIS/ESF/EurASC/AE. Image Processing Lab (IPL). Universitat de ValènciaVerified email at uv.es
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr
TianCheng LiProfessor, School of Automation, Northwestern Polytechnical UniversityVerified email at usal.es
Steven Van VaerenberghAssistant professor, University of CantabriaVerified email at unican.es
Pau ClosasAssociate Professor of ECE, Northeastern UniversityVerified email at northeastern.edu
Jesse ReadÉcole PolytechniqueVerified email at polytechnique.edu
Milos KrsticProfessor, University Potsdam; Department Head, IHP, Frankfurt (Oder) GermanyVerified email at ihp-microelectronics.com
Ruth KingThomas Bayes' Chair of Statistics, University of EdinburghVerified email at ed.ac.uk
Deniz AkyildizDepartment of Mathematics, Imperial College LondonVerified email at imperial.ac.uk
Simo SärkkäAssociate Professor, Aalto UniversityVerified email at aalto.fi
Mateu SbertUniversity of GironaVerified email at udg.edu
Brian M SadlerArmy Research LaboratoryVerified email at mail.mil
David RamírezAssociate Professor, Universidad Carlos III de MadridVerified email at uc3m.es
Ken NewmanBiomathematics and Statistics of Scotland and School of Mathematics, University of EdinburghVerified email at bioss.ac.uk
Oskar KvimanPh.D. student Machine Learning, KTHVerified email at kth.se

Víctor Elvira

Reader in Statistics and Data Science, University of Edinburgh

Verified email at ed.ac.uk - Homepage

Bayesian inference computational statistics Monte Carlo methods statistical signal processing machine learning


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Adaptive importance sampling: The past, the present, and the future MF Bugallo, V Elvira, L Martino, D Luengo, J Miguez, PM Djuric IEEE Signal Processing Magazine 34 (4), 60-79, 2017	208	2017
Effective sample size for importance sampling based on discrepancy measures L Martino, V Elvira, F Louzada Signal Processing 131, 386-401, 2017	178	2017
Generalized multiple importance sampling V Elvira, L Martino, D Luengo, MF Bugallo Statistical Science 34 (1), 129-155, 2019	159	2019
Cooperative parallel particle filters for online model selection and applications to urban mobility L Martino, J Read, V Elvira, F Louzada Digital Signal Processing 60, 172-185, 2017	144	2017
Accelerating MCMC algorithms CP Robert, V Elvira, N Tawn, C Wu Wiley Interdisciplinary Reviews: Computational Statistics 10 (5), e1435, 2018	127	2018
Layered adaptive importance sampling L Martino, V Elvira, D Luengo, J Corander Statistics and Computing 27, 599-623, 2017	123	2017
Improving population Monte Carlo: Alternative weighting and resampling schemes V Elvira, L Martino, D Luengo, MF Bugallo Signal Processing 131, 77-91, 2017	97	2017
An adaptive population importance sampler: Learning from uncertainty L Martino, V Elvira, D Luengo, J Corander IEEE Transactions on Signal Processing 63 (16), 4422-4437, 2015	97	2015
A survey of Monte Carlo methods for parameter estimation D Luengo, L Martino, M Bugallo, V Elvira, S Särkkä EURASIP Journal on Advances in Signal Processing 2020 (1), 1-62, 2020	95	2020
Orthogonal parallel MCMC methods for sampling and optimization L Martino, V Elvira, D Luengo, J Corander, F Louzada Digital Signal Processing 58, 64-84, 2016	90	2016
Efficient multiple importance sampling estimators V Elvira, L Martino, D Luengo, MF Bugallo IEEE Signal Processing Letters 22 (10), 1757-1761, 2015	88	2015
Metropolis sampling L Martino, V Elvira arXiv preprint arXiv:1704.04629, 2017	80	2017
Adapting the number of particles in sequential Monte Carlo methods through an online scheme for convergence assessment V Elvira, J Míguez, PM Djurić IEEE Transactions on Signal Processing 65 (7), 1781-1794, 2016	75	2016
Group importance sampling for particle filtering and MCMC L Martino, V Elvira, G Camps-Valls Digital Signal Processing 82, 133-151, 2018	73	2018
Rethinking the effective sample size V Elvira, L Martino, CP Robert International Statistical Review 90 (3), 525-550, 2022	57*	2022
A general criterion for analog Tx-Rx beamforming under OFDM transmissions J Via, I Santamaria, V Elvira, R Eickhoff IEEE Transactions on Signal Processing 58 (4), 2155-2167, 2010	57	2010
Heretical multiple importance sampling V Elvira, L Martino, D Luengo, MF Bugallo IEEE Signal Processing Letters 23 (10), 1474-1478, 2016	55	2016
Local-diffusion-based distributed SMC-PHD filtering using sensors with limited sensing range T Li, V Elvira, H Fan, JM Corchado IEEE Sensors Journal 19 (4), 1580-1589, 2018	52	2018
Advances in Importance Sampling V Elvira, L Martino Wiley StatsRef: Statistics Reference Online, 1-14, 2021	43	2021
The recycling Gibbs sampler for efficient learning L Martino, V Elvira, G Camps-Valls Digital Signal Processing 74, 1-13, 2018	43	2018

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