no code implementations • 11 Feb 2025 • Fernando Llorente, Luca Martino
The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density.
no code implementations • 19 May 2022 • Simo Alami. C, Fernando Llorente, Rim Kaddah, Luca Martino, Jesse Read
We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
1 code implementation • 7 Apr 2022 • Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino, Valero Laparra, Alvaro Moreno, Gustau Camps-Valls
Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling.
no code implementations • 7 Jan 2022 • Fernando Llorente, Luca Martino, Jesse Read, David Delgado-Gómez
In this work, we analyze the noisy importance sampling (IS), i. e., IS working with noisy evaluations of the target density.
no code implementations • 18 Jul 2021 • Luca Martino, Víctor Elvira, Javier López-Santiago, Gustau Camps-Valls
In many inference problems, the evaluation of complex and costly models is often required.
no code implementations • 18 Jul 2021 • Luca Martino, Víctor Elvira
In its basic version, C-MC is strictly related to the stratification technique, a well-known method used for variance reduction purposes.
no code implementations • 16 Apr 2021 • Daniel Heestermans Svendsen, Maria Piles, Jordi Muñoz-Marí, David Luengo, Luca Martino, Gustau Camps-Valls
We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding.
no code implementations • 11 Dec 2020 • Alvaro Moreno-Martinez, Marco Maneta, Gustau Camps-Valls, Luca Martino, Nathaniel Robinson, Brady Allred, Steven W Running
Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions.
no code implementations • 7 Dec 2020 • Gustau Camps-Valls, Luca Martino, Daniel H. Svendsen, Manuel Campos-Taberner, Jordi Muñoz-Marí, Valero Laparra, David Luengo, Francisco Javier García-Haro
However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only.
no code implementations • 7 Dec 2020 • Jorge Vicent, Luis Alonso, Luca Martino, Neus Sabater, Jochem Verrelst, Gustau Camps-Valls
Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $\sim$75\% and (2) the maximum interpolation relative errors by 0. 5\% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.
no code implementations • 18 Oct 2020 • Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés, Álvaro Moreno-Martínez, Adrián Pérez-Suay, Jose Adsuara, Irene Martín, Maria Piles, Jordi Muñoz-Marí, Luca Martino
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
no code implementations • 19 Sep 2020 • Luca Martino, Jesse Read
Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering.
no code implementations • 17 May 2020 • Fernando Llorente, Luca Martino, David Delgado, Javier Lopez-Santiago
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing.
no code implementations • 13 Dec 2019 • Daniel Heestermans Svendsen, Luca Martino, Gustau Camps-Valls
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest.
no code implementations • 18 Jul 2019 • Jesse Read, Luca Martino
A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features.
no code implementations • 27 Jan 2018 • Luca Martino
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data.
no code implementations • 14 Nov 2017 • Daniel Heestermans Svendsen, Luca Martino, Manuel Campos-Taberner, Francisco Javier García-Haro, Gustau Camps-Valls
Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models).
no code implementations • 15 Apr 2017 • Luca Martino, Victor Elvira
Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems.
no code implementations • 21 Nov 2016 • Luca Martino, Victor Elvira, Gustau Camps-Valls
The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions.
1 code implementation • 27 Sep 2016 • Jesse Read, Luca Martino, Jaakko Hollmén
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.
no code implementations • 9 Nov 2012 • Jesse Read, Luca Martino, David Luengo
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems.