203 citations · 2.2k across the 52 of their papers we have counts for
53 papers
Effective sample size approximations as entropy measures
L. Martino, V. Elvira
In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling algorithms, and discuss a possible extended range of applications. We show the rela…
Target-aware Bayesian inference via generalized thermodynamic integration
F. Llorente, L. Martino, D. Delgado
In Bayesian inference, we are usually interested in the numerical approximation of integrals that are posterior expectations or marginal likelihoods (a.k.a., Bayesian evidence). In…
Spectral information criterion for automatic elbow detection
L. Martino, R. San Millan-Castillo, E. Morgado
We introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion…
Universal and Automatic Elbow Detection for Learning the Effective Number of Components in Model Selection Problems
E. Morgado, L. Martino, R. San Millan-Castillo
We design a Universal Automatic Elbow Detector (UAED) for deciding the effective number of components in model selection problems. The relationship with the information criteria wi…
Polycrystalline MnBi as a transverse thermoelectric material
Alessandro Sola, Elena Olivetti, Luca Martino +1
To assess the potential of polycrystalline MnBi as a transverse thermoelectric material, we have experimentally investigated its anomalous Nernst effect (ANE) by means of the heat…
Gradient-based Adaptive Importance Samplers
Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz +1
Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of intractable integrals, very often involving a target probability density function. The perfo…
On the safe use of prior densities for Bayesian model selection
F. Llorente, L. Martino, E. Curbelo +2
The application of Bayesian inference for the purpose of model selection is very popular nowadays. In this framework, models are compared through their marginal likelihoods, or the…
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Simo Alami. C, Fernando Llorente, Rim Kaddah +2
Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal…
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen, Daniel Hernández-Lobato, Luca Martino +3
Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the…
Optimality in Noisy Importance Sampling
Fernando Llorente, Luca Martino, Jesse Read +1
In this work, we analyze the noisy importance sampling (IS), i.e., IS working with noisy evaluations of the target density. We present the general framework and derive optimal prop…
A Bayesian inference and model selection algorithm with an optimisation scheme to infer the model noise power
J. Lopez-Santiago, L. Martino, J. Miguez +1
Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate…
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…
Automatic tempered posterior distributions for Bayesian inversion problems
L. Martino, F. Llorente, E. Curbelo +2
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. Mo…
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
Luca Martino, Víctor Elvira, Javier López-Santiago +1
In many inference problems, the evaluation of complex and costly models is often required. In this context, Bayesian methods have become very popular in several fields over the las…
Compressed Monte Carlo with application in particle filtering
Luca Martino, Víctor Elvira
Bayesian models have become very popular over the last years in several fields such as signal processing, statistics, and machine learning. Bayesian inference requires the approxim…
Advances in Importance Sampling
Víctor Elvira, Luca Martino
Importance sampling (IS) is a Monte Carlo technique for the approximation of intractable distributions and integrals with respect to them. The origin of IS dates from the early 195…
Interpolation and Gap Filling of Landsat Reflectance Time Series
Alvaro Moreno-Martinez, Marco Maneta, Gustau Camps-Valls +4
Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions. Image fusion in general and downscaling/blending in particu…
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…
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Jorge Vicent, Luis Alonso, Luca Martino +3
Atmospheric correction of Earth Observation data is one of the most critical steps in the data processing chain of a satellite mission for successful remote sensing applications. A…
Living in the Physics and Machine Learning Interplay for Earth Observation
Gustau Camps-Valls, Daniel H. Svendsen, Jordi Cortés-Andrés +7
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables…
Deep Importance Sampling based on Regression for Model Inversion and Emulation
F. Llorente, L. Martino, D. Delgado +1
Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been wi…
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
Luca Martino, Jesse Read
The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of…
Adaptive quadrature schemes for Bayesian inference via active learning
F. Llorente, L. Martino, V. Elvira +2
Numerical integration and emulation are fundamental topics across scientific fields. We propose novel adaptive quadrature schemes based on an active learning procedure. We consider…
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
Fernando Llorente, Luca Martino, David Delgado +1
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability m…