Research area: Statistical signal processing - Bayesian Inference - Machine Learning.
Main topics of interest: Monte Carlo methods (MCMC, particle filtering), general problems and methods for Regression-Filtering-Smoothing,
Stochastic Processes (specially Gaussian processes), Multi-label Classification, Nonlinear Chaotic Systems.
(see Research for exhaustive information):
L. Martino, V. Elvira, G. Camps-Valls, "The Recycling Gibbs Sampler for Efficient Learning",
(to appear) Digital Signal Processing, 2017.
L. Martino, F. Louzada, "Adaptive Rejection Sampling with Fixed Number of Nodes",
Communications in Statistics - Simulation and Computation, 2017.
M. F. Bugallo, V. Elvira, L. Martino, D. Luengo, J. Miguez, P. M. Djuric, "Adaptive Importance Sampling: The Past, the Present, and the Future",
IEEE Signal Processing Magazine, Volume 34, Issue 4, Pages: 60-79, 2017.