196 citations · 371 across the 9 of their papers we have counts for
19 papers
Hamiltonian Adaptive Importance Sampling
Ali Mousavi, Reza Monsefi, Víctor Elvira
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated…
Graphical Inference in Linear-Gaussian State-Space Models
Víctor Elvira, Émilie Chouzenoux
State-space models (SSM) are central to describe time-varying complex systems in countless signal processing applications such as remote sensing, networks, biomedicine, and finance…
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
Oskar Kviman, Harald Melin, Hazal Koptagel +2
In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO…
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…
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…