Luca Martino

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92 článků

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Víctor ElviraProfessor in Statistics and Data Science, University of EdinburghE-mailová adresa ověřena na: ed.ac.uk
David LuengoUniversidad Politécnica de Madrid (UPM)E-mailová adresa ověřena na: upm.es
Gustau Camps-Valls (ORCID: 0000-00...Professor. Image Processing Lab (IPL). Universitat de ValènciaE-mailová adresa ověřena na: uv.es
Joaquin MiguezDepartment of Signal Theory & Communications, Universidad Carlos III de MadridE-mailová adresa ověřena na: tsc.uc3m.es
Jesse ReadÉcole PolytechniqueE-mailová adresa ověřena na: polytechnique.edu
Francisco LouzadaUniversidade de São PauloE-mailová adresa ověřena na: icmc.usp.br
Daniel Heestermans SvendsenDIS CopenhagenE-mailová adresa ověřena na: dis.dk
Jordi Muñoz-MaríProfessor. Electronic Engineering Department, University of ValenciaE-mailová adresa ověřena na: uv.es
Valero LaparraUniversitat de ValènciaE-mailová adresa ověřena na: uv.es
Fabrizio LeisenKing's College LondonE-mailová adresa ověřena na: kcl.ac.uk
Roberto CASARINCa' Foscari University of VeniceE-mailová adresa ověřena na: unive.it
Manuel Campos-TabernerUniversitat de ValènciaE-mailová adresa ověřena na: uv.es
F. Javier García HaroUniversity of ValenciaE-mailová adresa ověřena na: uv.es
Pablo M. OlmosUniversity Carlos III de MadridE-mailová adresa ověřena na: tsc.uc3m.es
Antonio Artés RodríguezProfessor, Dept. of Signal Theory and Communications, Universidad Carlos III de MadridE-mailová adresa ověřena na: tsc.uc3m.es
Jaakko HollménStockholm University, SwedenE-mailová adresa ověřena na: dsv.su.se
Ivan MartinoKTHE-mailová adresa ověřena na: kth.se
Carlos J. EscuderoProfessor of Computer Engineering, University of A CoruñaE-mailová adresa ověřena na: udc.es
Jeffrey S. RosenthalProfessor of Statistics, University of TorontoE-mailová adresa ověřena na: math.toronto.edu
Radu CraiuProfessor of Statistics, University of TorontoE-mailová adresa ověřena na: utoronto.ca

Sledovat

Luca Martino

Associate Professor

E-mailová adresa ověřena na: urjc.es - Domovská stránka

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


Název Seřadit podle citací Seřadit podle roku Seřadit podle názvu	Citace Citace	Rok
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	300	2017
Effective sample size for importance sampling based on discrepancy measures L Martino, V Elvira, F Louzada Signal Processing 131, 386-401, 2017	242	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	227	2020
Generalized multiple importance sampling V Elvira, L Martino, D Luengo, MF Bugallo	205	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	142	2014
Layered adaptive importance sampling L Martino, V Elvira, D Luengo, J Corander Statistics and Computing, 1-25, 2015	140	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
A review of multiple try MCMC algorithms for signal processing L Martino Digital Signal Processing 75, 134-152, 2018	113	2018
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	113	2015
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	111	2017
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	110	2023
Independent random sampling methods L Martino, D Luengo, J Míguez Springer, 2018	109	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	103	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	91	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	81	2021

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