196 citations · 1.2k across the 19 of their papers we have counts for
20 papers
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo, L. Martino, M. Bugallo +2
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either…
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
Daniel Heestermans Svendsen, Maria Piles, Jordi Muñoz-Marí +3
The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the dom…
Physics-Aware Gaussian Processes in Remote Sensing
Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen +5
Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has ex…
Heretical Multiple Importance Sampling
Víctor Elvira, Luca Martino, David Luengo +1
Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the imp…
Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes
Víctor Elvira, Luca Martino, David Luengo +1
Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in natu…
Generalized Multiple Importance Sampling
Víctor Elvira, Luca Martino, David Luengo +1
Importance Sampling methods are broadly used to approximate posterior distributions or some of their moments. In its standard approach, samples are drawn from a single proposal dis…