Luca Martino

Dikutip oleh

	Semua	Sejak 2020
Kutipan	5116	3430
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92 artikel

7 artikel

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Víctor ElviraProfessor in Statistics and Data Science, University of EdinburghEmail yang diverifikasi di ed.ac.uk
David LuengoUniversidad Politécnica de Madrid (UPM)Email yang diverifikasi di upm.es
Gustau Camps-VallsProfessor. Image Processing Lab (IPL). Universitat de ValènciaEmail yang diverifikasi di uv.es
Joaquin MiguezDepartment of Signal Theory & Communications, Universidad Carlos III de MadridEmail yang diverifikasi di tsc.uc3m.es
Jesse ReadÉcole PolytechniqueEmail yang diverifikasi di polytechnique.edu
Francisco LouzadaUniversidade de São PauloEmail yang diverifikasi di icmc.usp.br
Daniel Heestermans SvendsenDIS CopenhagenEmail yang diverifikasi di dis.dk
Jordi Muñoz-MaríProfessor. Electronic Engineering Department, University of ValenciaEmail yang diverifikasi di uv.es
Valero LaparraUniversitat de ValènciaEmail yang diverifikasi di uv.es
Manuel Campos-TabernerUniversitat de ValènciaEmail yang diverifikasi di uv.es
Fabrizio LeisenKing's College LondonEmail yang diverifikasi di kcl.ac.uk
Roberto CASARINCa' Foscari University of VeniceEmail yang diverifikasi di unive.it
F. Javier García HaroUniversity of ValenciaEmail yang diverifikasi di uv.es
Pablo M. OlmosUniversity Carlos III de MadridEmail yang diverifikasi di tsc.uc3m.es
Antonio Artés RodríguezProfessor, Dept. of Signal Theory and Communications, Universidad Carlos III de MadridEmail yang diverifikasi di tsc.uc3m.es
Jaakko HollménStockholm University, SwedenEmail yang diverifikasi di dsv.su.se
Ivan MartinoKTHEmail yang diverifikasi di kth.se
Carlos J. EscuderoProfessor of Computer Engineering, University of A CoruñaEmail yang diverifikasi di udc.es
Jeffrey S. RosenthalProfessor of Statistics, University of TorontoEmail yang diverifikasi di math.toronto.edu
Radu CraiuProfessor of Statistics, University of TorontoEmail yang diverifikasi di utoronto.ca

Ikuti

Luca Martino

Associate Professor

Email yang diverifikasi di urjc.es - Beranda

Bayesian inference computational methods (MCMC particle filters exact sampling etc.. )


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	304	2017
Effective sample size for importance sampling based on discrepancy measures L Martino, V Elvira, F Louzada Signal Processing 131, 386-401, 2017	244	2017
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, 1-62, 2020	233	2020
Generalized multiple importance sampling V Elvira, L Martino, D Luengo, MF Bugallo	207	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	159	2017
Efficient monte carlo methods for multi-dimensional learning with classifier chains J Read, L Martino, D Luengo Pattern Recognition 47 (3), 1535-1546, 2014	143	2014
Layered adaptive importance sampling L Martino, V Elvira, D Luengo, J Corander Statistics and Computing, 1-25, 2015	141	2015
Independent doubly adaptive rejection Metropolis sampling within Gibbs sampling L Martino, J Read, D Luengo IEEE Transactions on Signal Processing 63 (12), 3123-3138, 2015	133*	2015
Scalable multi-output label prediction: From classifier chains to classifier trellises J Read, L Martino, PM Olmos, D Luengo Pattern Recognition 48 (6), 2096-2109, 2015	115	2015
Marginal likelihood computation for model selection and hypothesis testing: an extensive review F Llorente, L Martino, D Delgado, J Lopez-Santiago SIAM review 65 (1), 3-58, 2023	113	2023
A review of multiple try MCMC algorithms for signal processing L Martino Digital Signal Processing 75, 134-152, 2018	113	2018
Orthogonal MCMC algorithms L Martino, V Elvira, D Luengo, A Artes-Rodriguez, J Corander 2014 IEEE Workshop on Statistical Signal Processing (SSP), 364-367, 2014	113*	2014
Improving population Monte Carlo: Alternative weighting and resampling schemes V Elvira, L Martino, D Luengo, MF Bugallo Signal Processing 131, 77-91, 2017	112	2017
Independent random sampling methods L Martino, D Luengo, J Míguez Springer, 2018	110	2018
Physics-aware Gaussian processes in remote sensing G Camps-Valls, L Martino, DH Svendsen, M Campos-Taberner, ... Applied Soft Computing 68, 69-82, 2018	107	2018
Efficient multiple importance sampling estimators V Elvira, L Martino, D Luengo, MF Bugallo IEEE Signal Processing Letters 22 (10), 1757-1761, 2015	101	2015
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	100	2015
Metropolis sampling L Martino, V Elvira arXiv preprint arXiv:1704.04629, 2017	94	2017
Rethinking the effective sample size V Elvira, L Martino, CP Robert International Statistical Review 90 (3), 525-550, 2022	92	2022
A survey of active learning for quantifying vegetation traits from terrestrial earth observation data K Berger, JP Rivera Caicedo, L Martino, M Wocher, T Hank, J Verrelst Remote Sensing 13 (2), 287, 2021	83	2021

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